Spatiotemporal variation analysis of regional flood disaster resilience capability using an improved projection pursuit model based on the wind-driven optimization algorithm

Journal of Cleaner Production 241 (2019) 118406

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Spatiotemporal variation analysis of regional flood disaster resilience capability using an improved projection pursuit model based on the wind-driven optimization algorithm Dong Liu a, b, c, d, 1, Jianping Feng a, 1, Heng Li a, b, c, d, *, Qiang Fu a, b, c, d, **, Mo Li a, Muhammad Abrar Faiz a, Shoaib Ali a, Tianxiao Li a, Muhammad Imran Khan e a

School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China c Heilongjiang Provincial Key Laboratory of Water Resources and Water Conservancy Engineering in Cold Region, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China d Key Laboratory of Water-Saving Agriculture of Ordinary University in Heilongjiang Province, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China e Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 April 2019 Received in revised form 9 September 2019 Accepted 11 September 2019 Available online 13 September 2019

Due to the weak methods available for evaluation of the resilience of regional flood disaster systems and the lack of research on the driving mechanism of resilience, by exploring the principles of regional flood disaster resilience and constructing a suitable evaluation index system, the wind driven optimization (WDO) algorithm was introduced, and an improved projection pursuit (PP) evaluation model of flood disaster resilience was proposed. Twelve farms under the Heilongjiang Agricultural Reclamation Hongxinglong Administration Bureau were included in the research area. A total of 43 primary indicators were selected from four criteria to describe the natural environment, culture, society, economic development and flood control technologies. The R clustering factor analysis method was used to determine 15 optimal indexes. The improved PP model based on the WDO algorithm (WDO-PP) was used to evaluate the flood disaster resilience of 12 farms. The results showed that the number of farms with a level IV rating on flood resilience decreased from 25% to 8.3% from 2002 to 2009. In 2009e2016, with the exception of the Bawuer and Shuguang farms, the flood disaster resilience index decreased, and that of the remaining farms increased. In 2002e2016, the Wujiuqi, Shuangyashan, Shuguang and Hongqiling farms in the central region of the Hongxinglong Administration Bureau were less resilient to disasters, and the farms that responded better to flood disasters were mainly located in the eastern or western Hongxinglong Administration Bureau near a river. Further analysis shows that the forest coverage rate, paddy field coverage ratio, shelter forest area ratio, proportion of primary industry, agricultural water use efficiency, and irrigation and drainage capacity were the key drivers of the flood disaster resilience in the Hongxinglong Management Bureau. Based on the Rastrigin and Schaffer functions, the results show that the success rate of the WDO algorithm is 100% over 10 iterations of the optimization calculation of the test function, while the success rate of the other two algorithms is relatively inadequate; however, in terms of value and standard deviation, both are better than adaptive particle swarm optimization (APSO) and adaptive genetic algorithm (AGA) algorithms. Moreover, in the convergence curve, the WDO algorithm converges fast, the number of iterations can achieve the optimal effect on average 3e5 times, and the AGA and APSO algorithms need more than 40 iterations to achieve the best-seeking effect. Taking the

Handling editor: Prof. Jiri Jaromir Kleme
s Keywords: Flood disaster resilience evaluation Index optimization Wind drive optimization Projection pursuit model Hongxinglong administration

* Corresponding author. School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China. ** Corresponding author. School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150030, China. E-mail addresses: [email protected] (D. Liu), [email protected] (J. Feng), [email protected] (H. Li), [email protected] (Q. Fu), limo0205@ 126.com (M. Li), [email protected] (M.A. Faiz), [email protected] (S. Ali), [email protected] (T. Li), [email protected] (M.I. Khan). 1 Co-first author. https://doi.org/10.1016/j.jclepro.2019.118406 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

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D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

index of agricultural water use efficiency in Bawuer farm as an example, the index weight is greater than 60% and the utilization rate of agricultural water is more than 98%, which is closer to reality. Therefore, the evaluation results of the flood disaster resilience evaluation model proposed in this study are more accurate: WDO-PP>(adaptive genetic algorithm) AGA-PP>(adaptive particle swarm optimization algorithm)APSO-PP. In conclusion, the WDO-PP model has certain reference value for flood disaster recovery, monitoring and early warning. © 2019 Elsevier Ltd. All rights reserved.

1. Introduction Since the 20th century, under the interference of human activities, the contradiction among regional populations, resources and the environment has become increasingly acute, resulting in considerable casualties (Patel and Srivastava, 2013) and property losses (Skakun et al., 2014). Due to the unreasonable use of natural resources, our ecosystems is damaged by man-made or natural disasters, such as greenhouse gas effects (Qasemi-Kordkheili and Nabavi-Pelesaraei, 2014) caused by an excessive dependence on fuel (Nabavi-Pelesaraei et al., 2014a,b), soil erosion (Liu et al., 2019) caused by vegetation destruction and slope land reclamation, and flood disasters caused by the reduction in soil and water conservation vegetation and defective water conservancy facilities. Among these disasters, flood disasters showed a sharp upward trend in frequency, range and loss degree, which has aroused considerable attention. In this context, identifying the resilience to regional flood disasters has become an urgent problem that must be solved to maintain the safe operation of water resource systems and promote human-water resource harmony. Resilience originated from the Latin word “resilio”, which was originally a basic concept of physics, and indicates that objects under pressure and have the ability to recover. In the early 1970s, resilience is defined as the system restoring force that includes the ability of a system to maintain its original characteristics, basic structure, and specific functions after some external disturbances (Steve et al., 2001). Holling introduced this concept into the study of natural ecosystems (Holling, 1973). The current resilience research mainly focuses on ecology (Le et al., 2018), social economics (Marchese, 2018) and other fields of disaster resilience (Cui et al., 2018). The study of the resilience to flood disaster is focused on how to limit external disturbance to flooding and transform the environmental system. The resilience to flood disaster refers to the ability of social systems (including individual, community, city, country and other systems of different scales) to adapt after being impacted by a flood disaster through external assistance and selfadjustment and to adjust to an orderly state as soon as possible. In recent years, research on the resilience evaluation of flood disaster systems has received increasing attention from scholars at home and abroad, and some beneficial research results have been obtained. For example, Sun et al. (2016) used Chaohu City as an example to evaluate the resilience capability of an area to return to normal after flood disasters. They dynamically analyzed the relationship between various influential factors through network processes and determined that resilience is primarily affected by natural dimensions. That work provided a valuable way to quickly find weak links in infrastructure, and ultimately, it will improve flood prevention and resilience in Chaohu City, Anhui Province, China. Vis et al. (2003) focused on the problem of flood risk management in the lower Rhine River in the Netherlands and proposed replacing the current resistance strategy with two resilience strategies, namely, detention in detention areas with different flood probabilities and Green River discharges. The impact of resilience strategy was evaluated in terms of economic, ecological and

landscape impact and flexibility; however, due to the uncertainty in data and prediction via modeling methods, the result is uncertain, so different strategies are only compared, and no quantitative analysis is carried out. Razafindrabe et al. (2015) assessed the community-scale flood disaster resilience in the Santa Rosa-Silang subbasin of Laguna Lake in the Philippines by integrating biophysical and socioeconomic indicators and by using disaster risk assessment guidelines, a climate disaster resilience index and other methods. The paper proposes a typical predisaster preparedness plan to strengthen the training of professionals after a disaster, unite the government and the people, and reduce the disaster cost. However, due to the lack of data, the qualitative assessment can be compared to those of similar regions and expert judgments only, and data selection is too subjective, lacking a scientific basis. Based on the screening of 24 resilience indicators, Kotzee and Reyers used principal component analysis (PCA) and Geographic Information System (GIS) techniques to measure and analyze the spatial distribution characteristics of flood resilience in three municipalities in South Africa. The results show that the resilience of major cities is high and that of surrounding areas is low. In PCA, when the sign of the factor load of the principal component changes, the meaning of the comprehensive evaluation function is not clear; therefore, this approach is not suitable for widespread application (Kotzee and Reyers, 2016). To study the factors affecting flood disaster resilience, Khunwishit et al. (2018) used the factors of disaster leadership as the entry point and interpreted disaster-resistant leadership abilities, including motivating employees, becoming familiar with the environment of disaster-stricken areas, sharing disasterresistant experiences, coordinating multiple municipal departments, innovating disaster-handling methods, dealing with problems efficiently and obtaining support, by means of multiple regression analysis. Therefore, the longer government leaders work in municipal areas, the stronger the disaster reduction and resilience ability of their cities. However, multivariate regression analysis may neglect the causal relationship between interaction and nonlinearity. As a result, the model of that work is singular, and the method is weak. Kamal et al. (2018) used focus group discussion, key information interviews, and household questionnaire surveys to collect data, taking the wetland area in northeastern Bangladesh as an example, analyzing flood resilience using regression modeling to show that the flood disaster resilience of low-income families is greater than that of high-income families and middleincome families. Religious beliefs can improve people's mental health and thus the resilience to flood disasters, while women with limited cultural and religious ties exhibit lower resilience. Because the statistical analysis method requires a high degree of data integrity and can reflect only the historical situation, ignoring the current changes, the regression model requires high stability of sample data, which easily leads to instability of the regression coefficient. Thus, the method presented in the abovementioned paper needs to be further optimized and improved. In summary, it is of great academic value to study the characterization of flood disaster resilience in depth. Assessment methods and models commonly used in

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

environmental problems include projection pursuit (PP) (Kruskal, 1969), extreme learning machine (Huang et al., 2006), and artificial neural network (Nabavi-Pelesaraei et al., 2014a,b). The PP model is widely used to evaluate the problem because it can transform high-dimensional raw data into a low-dimensional feature variable, thus reflecting the structural characteristic value of the original data. The PP model is essentially a dimension reduction technique. In the early 1970s, Kruskal (1972) proposed the PP theory. Friedman and Tukey (1974) studied the PP method in depth and clarified the idea of PP. Currently, the genetic algorithm (GA) (Berro et al., 2010) is widely used, and improved methods, including the adaptive genetic algorithm (AGA) (Huang, 2018.), have been developed to determine the optimal projection direction of the PP model. However, due to the lack of a complete convergence proof of the GA, the lag of theoretical research, the difficulty of parameter setting, and the lack of effective means to solve constrained optimization problems, there is a high risk of premature convergence and the computational load is much larger than that of traditional methods, even in solving multiobjective optimization problems (Nabavi-Pelesaraei et al., 2016). Current multiobjective evolutionary algorithms can effectively solve a number of targets that is generally not more than four. Particle swarm optimization (PSO) (Wu et al., 2009) and adaptive particle swarm optimization (APSO) (Patil and Kadwane, 2018) have been developed to determine the optimal projection direction of the PP model. However, for high-dimensional, nonlinear problems, the accuracy of the PP model is not high, the optimal projection direction is unstable, the algorithm parameters are uncertain and easily fall into the local optimum, and additional problems such as premature convergence occur. In the study of flood disaster resilience, project pursuit models are less used; thus, the wind driven optimization (WDO) algorithm (Bayraktar et al., 2013) is introduced to improve the project pursuit model. The updated wind drive speed equation is derived from a physics formula, which contains the influence of the real force on the velocity in nature and has actual physical significance, a strong global search ability and fast convergence speed. The WDO algorithm is used to search for the optimal projection direction of the model, and only the optimal projection direction value with a high precision, fast speed, and good stability can be obtained. This is a new used method to solve the optimal projection direction of the project pursuit model to avoid the generation of the local minimum problem in the optimization search process and to improve the optimal evaluation accuracy. According to the aforementioned literature, some studies are carried out using only cities and communities as the research areas, and less research is conducted in agricultural production areas. Furthermore, a few studies on the resilience to flood disasters remain at the conceptual level and the strategy response level, although some cases have been analyzed; however, the quantitative basis for the construction of the evaluation index system is insufficient, and the evaluation methods are more traditional. Because Heilongjiang Province, China, plays an important role in the national food security strategy, the area of current study covers 12 farms under the jurisdiction of the Hongxinglong industry in Heilongjiang Province, China. On the one hand, this work can quantify the regional characteristics and provide the basis for the study of influence factors and may guide local production practices. Additionally, a review of the methods in the flood disaster resilience literature shows that, to solve a single problem, it is necessary to quantify the research area mathematically. This paper builds a set of evaluation methods based on the model of PP, which can be used to evaluate the resilience to flood disaster in the study area. The research objectives of this paper are as follows: (1) Construct a suitable evaluation index system for regional flood disaster resilience, and use the R clustering factor analysis method

3

for the first time to screen the indicators used for flood disaster resilience evaluation. (2) Using the WDO algorithm to improve the disadvantages of the PP model, such as an unstable optimal projection direction, uncertain algorithm parameters, and a high risk of local optimization, evaluate the rationality and performance of the evaluation model. (3) Analyze and research the change in spatial distribution of regional flood disaster resilience and the possible causes of flood disaster resilience. (4) According to the actual situation of the study area, the WDO-PP model was used to screen the key influencing factors that could improve the flood disaster resilience of farms. This paper is organized as follows: after this introduction, the research methods, including the research scope, data sources, index selection and optimization algorithm, are detailed in Section 2. The research results are presented in Section 3, and the test algorithm performance is presented in Section 4. Finally, the research conclusions that were drawn are presented in Section 5.

2. Materials and methodology 2.1. Study area The Heilongjiang Agricultural Reclamation Area's Hongxinglong Administration Bureau area is located in the central and southern parts of the Sanjiang Plain, Heilongjiang Province, China (Zhang et al., 2019). The geographical coordinates are 129
55’~134
350 E and 45
35’~47
170 N, the east-west length is 330 km, the northsouth width is 170 km, and the total land area is 8808.5 km2. The Hongxinglong Administration Bureau is the third administration bureau of the Heilongjiang Reclamation Area. The Hongxinglong Administration Bureau, which controls the area between the elevations of 40e800 m, is bordered by the Wusuli River to the east, the Weiken River to the west, Wanda Mountain to the south, and the Songhua River and Naoli River to the north. Additionally, this area contains 12 farms, namely, the Erjiuyi, Wujiuqi, Bawuer, Bawusan, Raohe, Youyi, Shuangyashan, Jiangchuan, Shuguang, Beixing, Hongqiling, and Baoshan farms (Wu, 2014) (Fig. 1). The study area has a temperate continental monsoon climate. The average annual temperature is approximately 3.5
C, with precipitation concentrated in JuneeSeptember with an average annual precipitation of approximately 537 mm (Faiz et al., 2018a, 2018b, 2018c). Affected by topographic conditions, the Hongxinglong Administration Bureau has a radial water system and a dense water network with more than 30 rivers of different sizes (Yue et al., 2016); thus, the area is prone to flood disasters. In 2002e2016, the area of crops affected by flood disasters in the Hongxinglong Administration Bureau area accounted for 17.28% of the total affected area, which seriously affected the stable production of agriculture and the income of farmers. In this context, research on flood disaster resilience has become urgent to ensure the sustainable development of local agriculture.

2.2. Data sources Collected from the Hongxinglong Administration Bureau statistics on the economic and social development of the Hongxinglong Administration Bureau (2002e2016) (Li and Zhang, 2002e2016) and the Hongxinglong Administration Bureau's comprehensive annual report on water conservancy (2002e2016) (He et al., 2002e2016), the natural, economic and social development index data of the Hongxinglong Management Bureau were obtained for 2002, 2009 and 2016 for this study on flood disaster resilience.

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D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

Fig. 1. Geographical and administrative divisions including 12 state-owned farms of the Hongxinglong Administration.

2.3. Selection of initial indicators The changes in nature, society and economy, as well as the changes in the human expectations of science and technological levels, dictate that the resilience to a flood disaster is an ability to maintain the dynamic balance of the social-ecological composite systems and sustainable development. When a flood disaster occurs, resilience causes the composite system to absorb the disaster damage energy and protect the life, property, and safety of the residents and the corresponding infrastructure from disturbance; these actions occur through self-regulation and external energy input and do not compromise the long-term development of the composite system. Maintaining social function and ecosystem integrity (Wu et al., 2013), this ability is persistent but constantly changing. Based on a systematic selection of scientifically sound, representative, concise indicators (Lv et al., 2016) and on the analysis of the connotation of flood disaster resilience, the evaluation index system for flood disaster resilience is constructed in three levels (Liu et al., 2019), as shown in Fig. 2. The first level is the target level, i.e., the Hongxinglong Administration Bureau flood disaster resilience. The second level is the influence factor level, including the natural environment Xa, local society Xb, economic development Xc and flood control technology Xd, making it a fourcriteria layer. The third level is the indicator level, which contains 43 primary indicators in the four criterion levels (Table 1). 2.4. Index screening 2.4.1. Data standardization To reduce the impact of different indicator data and their dimensions on the selection of indicators, the use of indicator data standardization to process the data (Chi et al., 2011). 2.4.2. Indicator level information FilteringdR clustering analysis The R clustering analysis of the indexes in each criterion layer is carried out using the Ward method of system clustering. First, we calculated the total deviation squared sum of the criterion layer index and determined the final index quantity when and only when the total deviation was square and minimum (Yu and Yang, 2005).

Fig. 2. Flood disaster resilience evaluation index system hierarchy framework.

Hypothesis: the n evaluation indicators can be divided into m classes. Si is the deviation squared sum of the level i evaluation index (i ¼ 1,2, …,m); ni is the number of evaluation indicators for category i; Yi(j) is the sample value vector after the standardization of the jth evaluation indicator in category i (j ¼ 1,2, …,ni); and Y i is the sample average vector for level i indicators. The calculation formula is as follows: Sum of the squared deviations of the category i indicators:

Si ¼

ni  X

ðjÞ

Yi  Yi

0 

ðjÞ

Yi  Yi



j¼1

Total deviation squares sum of K categories:

(1)

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

5

Table 1 R Clustering-factor analysis index screening. Target level

Criterion level Number Indicator level

Resilience of flood disaster system

1 Natural environment criterion layer 2 (Xa) 3

type unit

þ




þ

mm/ year mm/ year %/year

8

The annual average temperature, Xa1 Average temperature per day of the year Average evaporation per day during Evaporation total, Xa2 the year Average daily precipitation during Total precipitation, Xa3 the year Average relative humidity per day of Average relative humidity, Xa4 the year The product of the energy Total energy consumption, Xa5 consumption and the corresponding equivalent value discount factor Surface area of surface in the area Water surface area, Xa6 Water production modulus, Xa7 The ratio of total water resources to land area The average green area per capita Per capita green area, Xa8

9

Soil erosion control area, Xa9

10 11

Forest coverage rate, Xa10 Total reservoir storage, Xa11

Take measures to reduce the area of soil erosion Ratio of forest area to land area Reservoir water content in the region at the end of the year Ratio of water field area to total area The ratio of forest protection planting area to total forest area Ratio of population to total land area

The ratio of the area of the people's leisure square to the total population Ratio of arable land to total Arable land per capita, Xb3 population Ratio of total grain production to Per capita grain possession, Xb4 total population The ratio of the actual use area of the Per capita housing area, Xb5 house to the total population Site construction road floor rate, Xb6 Ratio of construction land to total land area Physical quantity of fertilizer dosage The ratio of the actual amount of per unit cultivated area (Kouchaki- fertilizer to the area of cultivated land Penchah et al., 2017), Xb7 Number of means of transport per Number of vehicles per thousand 1000 people people, Xb8 Proportion of primary industry, Xc1 The ratio of the value added of primary industry to the value added of gross domestic product GDP density index (Li and Chen, (Regional GDP/Regional Land Area)/ 2003), Xc2 (National GDP/National Land Area) Income available to residents at their Per capita disposable income, Xc3 disposal Ratio of total agricultural output Density of agricultural output, Xc4 value to agricultural land area

4 5

6 7

12 13 Humanistic social standard layer (Xb) 14 15 16 17 18 19 20

21 Economic development layer (Xc)

Indicator Description

22

23 24 25

Paddy field coverage ratio, Xa12 Shelter forest area ratio, Xa13 Population density, Xb1 Cultural square per capita area, Xb2

e e

Y

0.713

N

0.817

N

2

0.648

N

1 2

0.016

0.885

N

þ e

2 2

0.914 0.829

N N

0.606

N

þ

hm2 104 m3/ hm2 hm2/ per hm2

0.599

N

þ þ

% 104 m3

3 4

0.796

0.805 0.696

Y N

þ þ

% %

4 5

e

0.959 e

Y Y

1

0.149

0.678

N

þ

hm2/ per m2

1

0.951

Y

þ

hm2

1

0.959

Y

þ

Tons

1

0.963

Y

2

0.994

N

þ

e

3

0.355

3

e

m

2

þ

%

2

0.955

N

e

Tons/ hm2

2

0.959

N

e

e

e

Y

þ

Vehicle/ 3 person % 1

0.076

0.836

Y

e

%

1

0.744

N

þ

yuan

2

0.932

N

þ

104 yuan/ hm2 104/ yuan ton/ hm2 104 yuan/ hm2 104 yuan/ hm2 %

2

0.914

N

2

0.919

N

2

0.845

N

2

0.843

N

e

27

PHGO, Xc6

28

Investment in water conservancy projects per unit area, Xc7

The ratio of investment in water conservancy projects to land area

29

Land economic density, Xc8

The ratio of total output value to land e area

30

Personal average GDP (Li and Chen, 2003), Xc9 Overall level of economic development (Li and Chen, 2003), Xc10 Agricultural investment ratio, Xc11

Ratio of GDP per capita in the region e to GDP per capita in the country pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi e Xc9  Xc2

Proportion of investment in flood control, Xc12

0.714

2

Investment in the management of þ floods Ratio of grain production to land area þ

33

0.796

tons of SCE

Flood control investment, Xc5

32

1

e

26

31

C

Factor Whether Cluster KeW to keep category Inspection load jasjj Sig

þ

The ratio of agricultural investment e to total investment The ratio of flood control and flood þ control investment to total investment in water conservancy

0.018

0.00

3

e

e

Y

4

0.235

0.794

N

4

0.802

N

%

4

0.845

Y

%

5

e

Y

e

(continued on next page)

6

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

Table 1 (continued ) Target level

Criterion level Number Indicator level

Indicator Description

type unit

Factor Whether Cluster KeW to keep category Inspection load jasjj Sig

Flood control technology layer (Xd)

(Total land area - other land area)/ Total land area Ratio of flood removal area to total area of flood area Water extraction to facilities in designated locations Land area irrigated by water saving Ratio of agricultural water consumption to total regional water consumption Ratio of total water resources to irrigation machinery Ratio of farmland area to population that can be properly irrigated in the year Ratio of crop planting area to arable land for years Total length of all embankment works The amount of water delivered per unit of time

þ

%

1

þ

%

þ

34

Land utilization rate, Xd1

35

Flood removal efficiency, Xd2

36

Electromechanical well, Xd3

37 38

Water-saving irrigation area, Xd4 Agricultural water use efficiency, Xd5

39 40

Drainage and irrigation machinery ability, Xd6 Per capita effective irrigated area, Xd7

41

Multiple cropping index, Xd8

42

Length of embankments, Xd9

43

Irrigation and drainage capacity, Xd10

0.942

N

1

0.978

N

set

1

0.899

N

þ þ

hm2 %

1 2

0.937 0.818

N Y

þ

m3/kw

2

0.752

N

þ

2

hm / person

3

0.863

Y

þ

%

3

0.582

N

þ

km

4

0.715

N

þ

3

4

0.929

Y

m /s

0.001

0.439

0.439

0.593

Note: In the table, "þ" means that the index promotes the development of flood disaster resilience, and "-" means the development of flood disaster resilience.

S¼

ni  K X X

ðjÞ Yi

 Yi

0 

ðjÞ Yi

 Yi



(2)

i¼1 j¼1

Recalculate formula (2) until the final classification number is m. 2.4.3. Rationality verification of R cluster NumberdK-W inspection The number of R clusters has a certain subjectivity. To avoid human error, it is necessary to use the nonparametric KeW inspection method for each criterion layer index after clustering to verify whether there are differences in the numerical value of each index. At the same time, the rationality of the clustering number is tested. Suppose the significant difference level of the test results of each criterion layer is Sig>0.05 (Guo, 2005). Then, there is no significant difference among the indicators after clustering, which indicates that the number of clusters is reasonable and effective; otherwise, reclustering is required.

the eigenvalues of the top K in the ranking. The relationship between lj and aij is as follows:

lj ¼

Xr

a2 i¼1 ij

(5)

Finally, the factor analysis method is used to filter the index. Because the factor load jaijj reflects the correlation between Yi and the common factor, as jaijj becomes larger, the influence of the selected index on the evaluation result will become more significant. Therefore, the index with the larger jaijj is selected.

2.4.4. Determine indicator information contentdfactor analysis Refer to the basic model of factor analysis (Geng et al., 2007):

2.4.5. Rationality determination of index system construction Because the variance in the final indicator system can reflect the information content of the original indicator system, the basis of judging the rationality of the evaluation index is as follows: S is the indicator data covariance matrix, tr S is the trace of the covariance matrix, s is the number of indicators after screening, and h is the number of broadly selected indicators. Then, the information contribution rate of the screened evaluation index to the extensive screening index system is as follows (Yu and Yang., 2005):

Yi ¼ ai1 F1 þ ai2 F2 þ / þ aiK FK þ mi

In ¼ tr S s =tr S h

(3)

where Yi is the i indicator in formula (i ¼ 1,2, …,r); Fj is the jth common factor (j ¼ 1,2, …,K); aij is the load of the i indicator on the jth public factor, called the factor load; mi is a special factor that affects only indicator Yi; K is the number of public factors; and r is the number of indicators. First, the coefficient matrix Rrr of the numerical value of the standardized index is determined, and the eigenvalue lj of the matrix R, where lj is the jth public factor Fj, is used to explain the total variance in the original indicator value. Thus, the variance contribution rate of Fj to the original indicator value is as follows:

uj ¼ l j

, Xk

l j¼1 j

(4)

Second, the eigenvalue lj results are sorted from the largest to the smallest, and the cumulative variance contribution rate is required to be  80% of the requirements; the factor analysis model is constructed by selecting the common factors corresponding to

(6)

The above formula means that the sum of the variance in the s indexes after screening and the ratio of extensive screening of the sum of the variance in the h indicators indicate that s selected evaluation indicators reflect the information of the h original widely screened indicators. 2.5. Improved PP model 2.5.1. Fundamentals of PP model As a method of clustering analysis in mathematical statistics, PP can be used simultaneously for exploratory analysis and deterministic analysis (Kruskal, 1969; Friedman and Stuetzle, 1981; Hall, 1989). It is used to project high-dimensional data into lowdimensional space and study the projected eigenvalues reflecting the high-dimensional data structure or features in the lowdimensional space to reduce dimensionality. This method can realize the function of objective weighting and evaluation and can eliminate the interference of small variables (Fu et al., 2002). The

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

process is as follows: Because the indicators were standardized during data screening, they are no longer repeated. The projection indicator function Q(a) is constructed. The processed data are synthesized into one-dimensional projection values with a ¼ {a1, a2, …, an} as the projection direction.

zðiÞ ¼

n X

aj xij

(7)

j¼1

The projection indicator function expression is:

Q ðaÞ ¼ Sz Dz

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP u n u ðzðiÞ  EðzÞÞ2 ti¼1

X

Fi

(13)

where r is the air density; a is the acceleration vector, and Fi is the force on the air molecule. There are 4 main forces in the air: the gravity force (FG), the pneumatic gradient force (FPG), the friction force (FF) and the Coriolis force (FC) produced by the rotation of the Earth.

FG ¼ rdg

(14)

FPG ¼  VP dV

(15)

FF ¼  rau

(16)

FC ¼  2Uu

(17)

(10) Pn 1

In the following formula, EðzÞ ¼ n i¼1 ZðiÞ, {z(i), i ¼ 1,2, …, n} is the projection value of the indicator values of sample i in linear space, and R is the local density window radius. The distance ri,j ¼ jz(i)-z(k)j, u(R-ri,j) between the indicator projection points of samples i and j is the unit step. This step is assumed to be 1 when R  ri,j; otherwise, it is assumed to be 0. Different projection directions reflect different data structure characteristics, and the optimal projection direction can be estimated by maximizing the projection index function, that is,

maxQ ðaÞ ¼ Sz ,Dz

a2j ¼ 1a2½1; 1

Therefore, Formula (13) can be rewritten as follows:

r

i¼1 j¼1

n X

ra ¼

(9)

n1

n X n  X    Dz ¼ R  rij ,u R  rij

s:t:

According to Newton's second law, the law of air molecular motion is as follows:

(8)

Here, Sz is the standard deviation of the projected value z(i). z(i) is used as the resilience index of flood disaster, and Dz is the local density of z(i), which is:

Sz ¼

7

(11)

(12)

j¼1

In the formula, Sz is the standard deviation, and Dz is the local density value. The problem can be transformed into a nonlinear optimization problem with {aj jj ¼ 1,2, …, n} as the optimization variable. Based on the projection tracking technology for highdimensional, nonlinear systems, there are problems, such as the accuracy of the optimization being too low, the amount of calculation being large, or the optimal solution of the best projection not existing (Diao and Cui, 2017), and the AGA model (Doostie et al., 2018) and APSO model are adopted in most cases to optimize the PP. However, the convergence rate of these two methods is slow, and the optimization effect is not ideal. Therefore, to achieve the goal of a good optimization effect, with a high result precision and a fast algorithm convergence speed, the intelligent algorithm of WDO in introduced because it has a strong global search ability, high precision, high optimization efficiency and strong robustness (Fan et al., 2017; Tian et al., 2017). 2.5.2. WDO algorithm The WDO algorithm is a naturally inspired method. The principle is based on using the air pressure difference to promote air flow and eventually achieve an equilibrium state, with each unit part of the air achieving equilibrium as a target value as the optimal solution of the new global optimization algorithm (Bayraktar et al., 2010). The algorithm principle is derived as follows:

Du ¼ ðrdVgÞ þ ð  VP dVÞ þ ð  rauÞ þ ð  2UuÞ Dt

(18)

In this formula, dV is the infinitely small volume of air; g is the gravitational acceleration; VP represents the air pressure gradient; a is the coefficient of friction; u represents the velocity vector of the air molecule; and U is the velocity of the rotation angle of the Earth. To simplify the model derivation, the WDO algorithm assumes a time difference of Dt ¼ 1, and the volume of the air molecule is dV ¼ 1; thus, Formula (18) can be reduced to

rDu ¼ ðrgÞ þ ð  VpÞ þ ð  rauÞ þ ð  2UuÞ

(19)

Meanwhile, according to the relationship between air pressure and temperature density: p ¼ rRT, where p is the pressure, R is the universal gas constant and T is the temperature. Formula (19) can be changed to:



Du ¼ g þ

 Vp

RT pcur

  au þ

  2U  uRT pcur

(20)

where pcur is the current air particle pressure value. Because Du ¼ unew-ucur, unew is the air particle velocity of the next iteration, and ucur is the air particle velocity of the current iteration, Formula (20) can be written as:

 unew ¼ ð1  aÞucur þ g þ

 Vp

RT pcur



 þ

2U  uRT pcur

 (21)

In the above formula, g ¼ jgj(0-xcur) (Bayraktar et al., 2013), and

Dp ¼ jpcur-poptj(xcur-xopt), so Formula (21) can be changed to:    1  unew ¼ ð1  aÞucur  gxcur þ RT  1 xopt  xcur i ! dim cuother cur þ i

(22)

In the above formula, xcur is the current position, xopt is the current optimal position of the air particle, and popt is the current dim is the speed in the current optimal pressure value. uother cur dimension of the air particle. Because c ¼ -2jUjRT (Chen et al., 2016), i is the rank of all air molecules. The position of air molecules is updated as follows:

xnew ¼ xcur þ ðunew DtÞ

(23)

8

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

Fig. 3. WDO-PP evaluation model flow of flood disaster resilience.

Because the position and speed of air molecules are uncertain within the range of air renewal, according to Formulas (20) and (21), the velocity and position of each molecule are randomly adjusted, just as the real air is free to move in general. Therefore, according to the above content, the updated result is the optimal solution.

2.5.3. WDO-PP model construction The steps of constructing the WDO algorithm (Bayraktar et al., 2010) to optimize the PP model are as follows (Bayraktar and Komurcu, 2016): Step 1 The evaluation index system of the flood disaster resilience is constructed, and the index is standardized. Step 2 The algorithm parameters are initialized, which includes defining the number of groups M, the maximum number of iterations T, and the maximum allowable wind speed u; a, g, RT and c are the friction coefficient, gravity acceleration, pressure gradient influence coefficient, and Coriolis force influence coefficient, respectively. The setup requires optimizing the problem dimension N, searching the space and algorithm termination conditions and solving the space random initialization of the air packet microspace position xi(i ¼ 1,2, …, N) and rate ui(i ¼ 1.2, …, N). Step 3 The target function is selected. Because the WDO algorithm needs to solve the minimum value, the inverse of Formulas (11) and (12) are used as the objective function, that is, Formula (24):

8 minQ 0 ðaÞ ¼ 1 ðSz ,Dz Þ > > < n X > a2j ¼ 1a2½1; 1 s:t: > :

(24)

j¼1

Step 4 Based on the flood disaster resilience evaluation sample and Formula (24), the fitness value of each air unit is calculated, and the optimal air unit x* of the current population is sorted and saved. Step 5 For t ¼ tþ1, the current fitness value is calculated by updating the position and velocity of the particle through Formulas (22) and (23). If the current fitness value is better than the previous value, then the best air unit in the current population x* is updated; otherwise, the former air unit is regarded as the current best air unit x*. Step 6 The optimal air unit individual fitness value and its spatial position x*, which is the optimal projection direction a, is output, and the algorithm ends. Step 7 The optimal projection direction a is substituted into Formula (7) in the regional flood restoring force index z(i); at the same time, the classification standard flood restoring force index z(s) (s is the grading standard, with reference to the literature (Gong et al., 2015), the flood disaster resilience level is divided into four levels, I, II, III, IV, as shown in Table 3) is determined, and the resilience to the flood disaster is evaluated, analyzed and sorted.

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

9

Table 2 Classification of flood disaster resilience levels. Index

IV

III

II

I

The annual average temperature Forest coverage rate Paddy field coverage ratio Shelter forest area ratio Cultural square per capita area Arable land per capita Per capita grain possession Number of vehicles per thousand people Proportion of primary industry Land economic density Agricultural investment ratio Proportion of investment in flood control Agricultural water use efficiency Per capita effective irrigated area Irrigation and drainage capacity

<3 <0.074 <0.090 <0.096 <0.494 <1.110 <7.898 <9.724 <0.542 >2.119 >0.556 <0.051 <0.986 <0.217 <7

3e3.5 0.074e0.187 0.090e0.232 0.096e0.354 0.494e0.863 1.110e1.510 7.898e10.592 9.724e14.204 0.542e0.587 2.119e1.311 0.556e0.313 0.051e0.193 0.986e0.994 0.217e0.702 7e35

3.5e4.1 0.187e0.272 0.232e0.346 0.354e0.844 0.863e2.300 1.510e1.930 10.592e13.990 14.204e19.604 0.587e0.657 1.311e0.688 0.313e0.205 0.193e0.489 0.994e0.996 0.702e1.336 35e60

>4.1 >0.272 >0.346 >0.844 >2.300 >1.930 >13.990 >19.604 >0.657 <0.688 <0.205 >0.489 >0.996 >1.336 >60

The specific implementation process of the WDO-PP model is shown in Fig. 3. 3. Results and analysis 3.1. Optimization of indicators For the preliminary selection of the evaluation index for flood disaster resilience of the Hongxinglong Administration Bureau study area, a total of 43 primary election indicators are shown in column 4 of Table 1, and the explanation of the indicators is shown in Table 1. Taking the natural environment guideline layer as an example, first, the data are subjected to positive and negative standardization according to column 6 of Table 1. Second, the data processing is grouped into 5 categories. This process can be implemented with the help of SPSS software. The results are listed in Table 1, column 8. The 8 indicators of the cultural and social norms are grouped into 3 categories. The 12 indicators of the economic development criterion layer are grouped into 5 categories. The 10 indicators of the flood control technical criterion layer are grouped into 4 categories, and the results are listed in the 14th to 43rd rows of Table 1 and the 8th column, respectively. Taking the KeW inspection of the natural environment guideline layer as an example, the KeW inspection process is illustrated. Metrics that will be clustered into the same category, such as the Xa1 and Xa2 standardized data into the SPSS software, are selected for the nonparametric KeW test; the software automatically outputs the KeW test results with Sig ¼ 0.796. Similarly, other indicators of the same category are substituted into the software and listed in the 9th column of Table 1. Since Sig is significantly larger than the critical value of 0.05, the clustering process is correct. For the values of the standardized indicators, according to the respective categories of the generation Formulas (3) to (5), the SPSS software automates the factor analysis process, and the factor load jaijj for the analysis results are shown in column 10 of Table 1. The largest indicator of jaijj in each category is retained because it has the greatest impact on the evaluation results. Notably, when the cluster category of a single type of indicator represents an aspect of the evaluation system, it is directly selected, e.g., the Xa13 layer of the natural environment guidelines; thus, the indicators Xb8, Xc8, and Xc12 are selected. In summary, the final screening retains 15 preferred indicators, as shown in column 11 of Table 1 “Y". Thus, the 15 preferred indicators eventually screened include the following: Natural environment criterion layer: the annual average

Table 3 Level of simulation interval of flood disaster resilience evaluation model. Level

WDO-PP projection Interval

I II III IV

0.9512 [0.4032, 0.9512) [0.2655, 0.4032) <0.2655

temperature (Xa1), forest coverage rate (Xa10), paddy field coverage ratio (Xa12), and shelter forest area ratio (Xa13). Humanistic social standard layer: cultural square per capita area (Xb2), arable land per capita (Xb3), per capita grain possession (Xb4), and number of vehicles per thousand people (Xb8). Economic development criteria layer: proportion of primary industry (Xc1), land economic density (Xc8), agricultural investment ratio (Xc11), and proportion of investment in flood control (Xc12). Flood control technology criterion layer: agricultural water use efficiency (Xd5), per capita effective irrigated area (Xd7), and irrigation and drainage capacity (Xd10). In ¼ trSε/trSg ¼ 5.74  108/7.68  108 ¼ 75.28% Thus, the selected evaluation index system can use 34.88% (15/ 43 ¼ 0.34883) of the indicators to reflect the primary evaluation index system, with more than 75% of the original information. 3.2. Determine evaluation criteria Currently, the evaluation of flood disaster resilience at home and abroad has not resulted in a single standard (for more details, see Sun et al., 2011). Combined with the actual situation of the Hongxinglong Administration Bureau, the evaluation criteria for the resilience level of flood disaster were determined, as shown in Table 2. 3.3. Analysis of temporal and spatial characteristics of resilience The critical index values of each level in Table 2 are the normalized processed data that were substituted into the WDO-PP evaluation model constructed above; finally, the model corresponding to the level interval data is obtained. The results are shown in Table 3. The optimal evaluation index values of each farm in the Hongxinglong Administration Bureau study area in Table 1 are normalized according to Formulas (1) and (2). The normalized data

10

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

are input into the WDO-PP flood disaster resilience evaluation model, and the projection direction (weight) of the flood disaster resilience index for each farm is obtained (see Table 4). At the same time, the data are included in the WDO-PP model, and the resilience index of flood disaster and the corresponding flood disaster resilience level change are obtained (see Table 5, Fig. 4, and Fig. 4). Table 4 shows that different indexes have different impacts on the improvement of the flood disaster resilience of each farm: for example, the forest coverage rate is an important index affecting the resilience to flood disasters in the Wujiuqi, Bawusan, Raohe, Shuangyashan, Shuguang and Beixing farms. Therefore, to improve the above farm flood disaster resilience, it is necessary to expand the afforestation area. The paddy field coverage ratio is an important index affecting the resilience to flood disasters in the Wujiuqi, Shuangyashan and Baoshan farms. Therefore, to improve the flood disaster resilience of the above three farms, the planting area of paddy fields can be expanded, and the input of paddy field funds can be increased. The shelter forest area ratio is an important index affecting the resilience to flood disasters in the Youyi, Bawusan and Erjiuyi farms. Therefore, to improve the resilience to flood disasters in the above three farms, it is necessary to improve the diversity of the shelterbelts, expand the area of forest shelter planting, and prohibit the exploitation of shelterbelts and other measures. The proportion of primary industry is an important index affecting the flood disaster resilience in the Wujiuqi, Bawuer, Jiangchuan and Baoshan farms. Therefore, to improve the resilience to flood disasters in the four farms mentioned above, it is necessary to expand the investment in agroforestry and pastoral fisheries as the primary industry, increase the proportion of primary-industry investments, and promote local economic development. The agricultural water use efficiency is an important influencing factor for disaster resilience in the Youyi, Bawuer, Raohe, Jiangchuan and Shuguang farms. Therefore, to improve the resilience of the above farms’ flood disaster response, it is necessary to reasonably increase the actual agricultural water consumption, avoid waste problems caused by the occurrence of evaporation and water leakages, improve the efficiency of use and improve farmers' awareness of water conservation. Improving the irrigation and drainage capacity is an effective means to improve the resilience to flood disasters in the Baoshan farm. To increase the resilience to flood disasters, the Baoshan farm should increase its capital investment in irrigation and drainage machinery and equipment to improve the efficiency of irrigation and drainage and the flood control infrastructure and to decrease waterlogging. To further explore the change law of flood disaster resilience, the radar image map is drawn on the basis of Table 5, as shown in Fig. 4. According to Fig. 4, the graphic area is positively correlated with

Table 5 Flood resilience index and corresponding grade trends of farms in the Hongxinglong Administration according to the WDO-PP model. Farm

Year 2002

Year 2009

Year 2016

Resilience level change

Youyi Wujiuqi Bawuer Bawusan Raohe Erjiuyi Shuangyashan Jiangchuan Shuguang Beixing Hongqiling Baoshan

0.6441 0.2295 0.904 0.3989 1.0461 1.8729 0.2457 2.7482 0.3868 0.3997 0.2541 2.485

0.6882 0.2575 0.9512 0.4149 1.0367 1.8744 0.2655 2.7951 0.4032 0.4045 0.2829 2.4689

0.704 0.2568 0.9375 0.4513 1.0415 1.8782 0.2724 2.8069 0.3825 0.4052 0.2821 2.573

II IV II/I/II III/II I I IV/III I III/II/III III/II IV/III I

Fig. 4. Variation map of the flood disaster resilience at different stages of the Hongxinglong Administration Bureau farms under the WDO-PP model. Note: The polygon's vertex is marked with 12 farm names.

flood disaster resilience. The order of the rate of change in the of flood resilience of the 12 Hongxinglong Administration farms studied is as follows: 2002e2016 rate of change >2002e2009 rate of change >2009e2016 rate of change. The flood disaster resilience level in the study area has increased significantly with time before

Table 4 The weight of the flood disaster resilience index of each farm in the WDO-PP algorithm. Index

Youyi

Wujiuqi

Bawuer

Bawusan

Raohe

Erjiuyi

Shuangyashan

Jiangchuan

Shuguang

Beixing

Hongqiling

Baoshan

Xa1 Xa10 Xa12 Xa13 Xb2 Xb3 Xb4 Xb8 Xc1 Xc8 Xc11 Xc12 Xd5 Xd7 Xd10

0.011598 0.010505 0.031616 0.709273 0.001909 0.02738 0.000698 0.002344 0.014353 0.000188 0.007743 0.001938 0.179872 2.41E-05 0.000559

0.000318 0.653973 0.175386 0.002067 0.001225 0.003473 0.003617 0.001579 0.101431 0.016724 0.009068 0.004345 0.024695 0.002026 7.29E-05

0.011619 0.036549 0.00498 0.064331 0.00068 0.023241 0.000598 0.004946 0.111682 0.005005 0.00559 0.09085 0.626619 0.011411 0.0019

0.010524 0.56426 0.078682 0.238522 0.000696 0.001316 0.000374 0.000283 0.00112 0.001428 0.036674 0.006451 0.036986 0.020779 0.001903

0.010524 0.56426 0.078682 0.238522 0.000696 0.001316 0.000374 0.000283 0.00112 0.001428 0.036674 0.006451 0.036986 0.020779 0.001903

0.000695 0.113676 0.069922 0.007062 0.096093 0.001138 0.000188 0.000842 0.038534 0.00193 0.016847 0.001849 0.64645 0.003169 0.001604

0.00105 0.002669 0.003276 0.872696 0.000474 0.018633 0.000433 0.000135 0.074778 0.001132 0.002764 0.004157 0.001805 0.001034 0.014963

0.003055 0.435685 0.465094 0.003609 0.050959 0.005142 0.00443 0.001155 0.001941 0.002103 0.008931 0.005025 0.002537 0.007893 0.002441

0.007407 0.041917 0.045126 0.006939 0.013634 0.003699 0.000645 0.000885 0.13128 0.001602 0.022681 0.002014 0.634767 0.055781 0.031622

0.000314 0.690831 0.000653 0.003592 0.001846 0.001849 0.00128 0.00032 0.018665 0.001076 0.006493 0.003857 0.268007 0.000604 0.000614

0.001149 0.960853 0.001495 0.028501 0.005277 2.07E-05 1.03E-05 2.11E-05 0.000284 2.31E-05 0.000846 3.02E-05 0.000278 0.000183 0.001028

6.17E-05 0.976757 1.66E-05 9.65E-05 0.000116 0.014893 9.22E-05 8.77E-05 0.000134 0.000102 0.001426 0.004837 0.00015 0.000837 0.000394

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

2009. However, in the 2009e2016 period, the resilience to flood disasters declined. Although some areas have regressed, the overall level is still rising. From a geospatial point of view, the flood disaster resilience responses of the 12 farms from 2002 to 2016 has unique characteristics. There was no significant change in flood resilience of the Raohe and Erjiuyi farms. The trend of flood resilience of the Youyi, Shuangyashan and Bawusan farms has been steadily increasing.

11

The Jiangchuan, Beixing, Hongqiling and Wujiuqi farms’ flood recovery capacity was first improved and then remained stable. The trend of flood disaster resilience of the Baoshan farm decreased first and then increased. The trend of flood resilience of the Shuguang and Bawuer farms increased first and then decreased. However, the difference among the Shuguang and Bawuer farms is that the flood disaster resilience of the Bawuer farm in 2016 was higher than that in 2002, while that of the Shuguang farm in 2016

Fig. 5. Spatial and temporal distribution of flood disaster resilience of the Hongxinglong Management Bureau under the WDO-PP model: (a) 2002, (b) 2009, (c) 2016.

12

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

4. Discussion

was lower than that in 2002. To more intuitively show the spatial distribution of flood disaster resilience of the Hongxinglong Administration study area, the spatial distribution of flood resilience in various farms in each year is plotted, as shown in Fig. 5. As shown in Fig. 5(a), the degree of resilience to floods in farms adjacent to rivers is generally high. The Hongxinglong Management Bureau is located in an area with a temperate continental monsoon climate, and the climate varies greatly from season to season. The central farms do not receive abundant precipitation, and there are fewer types of vegetation, such as trees. Thus, these farms have the disadvantages of weak flood control and disaster resistance and thus low flood disaster resilience. According to Fig. 5(a) and (b), among the 12 farms of the Hongxinglong Management Bureau, 50% of the farms have improved their flood disaster resilience, and 75% of the farms have level I and level II flood disaster resilience. The change in the resilience to a specific flood disaster is as follows: Bawuer farm flood disaster resilience levels increased to level I; Bawusan, Shuguang, and Beixing farm flood disaster resilience levels increased to level II; and the Shuangyashan and Hongqiling farm flood disaster resilience levels increased to level III. Fig. 5(b) and (c) show that the general spatial pattern of the flood disaster resilience level of the 12 farms of the Hongxinglong Management Bureau shows a trend of gradually weakening from north to south and from both sides to the middle. Among the 12 farms of the Hongxinglong Administration Bureau, the ranking of flood disaster resilience from best to worst is as follows: the Jiangchuan, Baoshan, Raohe and Erjiuyi farms, level I; the Youyi, Bawuer, Bawusan and Beixing farms, level II; the Shuangyashan, Hongqiling and Shuguang farms, level III; finally, the Wujiuqi farm is ranked last, level IV. Fig. 5(a) and (c) show that the flood disaster resilience level of the 12 farms of the Hongxinglong Administration Bureau is dominated by levels I and II and that the number level II farms are gradually increasing. As the number of level IV farms reduces from three to one, the overall level of flood disaster resilience of the Hongxinglong Management Bureau is gradually improving. To improve the level of flood disaster resilience, it is suggested that the farms evaluated as level I should expand afforestation and the paddy field planting area to enhance the soil and water conservation ability of the forest when flood disasters occur. Farms rated as level II farms, in addition to the above aspects, should also improve the proportion of protected forest area, reduce the waste of water resources in related industries, and improve water efficiency. For a grade III farm, in addition to the above measures, the ratio of input to output of the primary industry should be increased to form a system of agriculture, forestry, animal husbandry and fishery to promote the healthy and sustainable development of the farm. In addition, the farm evaluated as IV should consider all the above mitigation measures. At the same time, we should expand the construction of cultural infrastructure, scientifically improve food output, promote the application of flood control and waterlogging machinery technology, improve production via science and technology, and promote the sustainable development of ecological benefits.

4.1. Algorithm performance test To verify the rationality of the algorithm, two sets of typical test objective functions were used to test the simulation training (Diao and Cui, 2017), and the minimum value of the test function is shown in Table 6. The Rastrigin function has more local minima, and there is only one global minimum at the [0,0] point, at which the function equals zero, which is often used to test algorithm optimization and searches. Schaffer is a multipeak function that is commonly used to test global and local search balance performance and the ability to jump out of the local extremum. The specific parameters are set as follows: The three algorithms are implemented in the M language by using MATLAB 2010. The population number of the WDO algorithm is 20, and the maximum number of iterations is 500; a ¼ 0.85, g ¼ 0.65, RT ¼ 1.5, and c ¼ 0.1. The mutation probability is pm ¼ 0.1. The APSO algorithm is set with a maximum weight of 1.2, a minimum weight of 0.5, and a learning factor of c1 ¼1, c2 ¼ 1. The AGA is based on an adaptive crossover probability coefficient of 0.8 APSO > AGA. Although the optimization time of the WDO is longer than those of the APSO and AGA, the success rate of the WDO algorithm is 100% when comparing the two optimization functions, which is significantly higher than that of the APSO and AGA. (3) To summarize, the WDO algorithm has a better precision, convergence speed, global extremum optimization ability and robust performance than those of the APSO and AGA; additionally, the WDO algorithm draws the convergence curve of the intelligent algorithm under different functions, as shown in Fig. 6. Under the test of the two functions, the WDO algorithm decreases in terms of the fitness value the fastest, the fitting effect is

Table 6 Testing objective functions. Functions

Expressions

Dimension

Value range

Theoretical optimal solution

Rastrigin Schaffer

Ras(x) ¼ 20 þ x21þx22-10(cos2px1þcos2px2) qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ðsin x21 þ x22 Þ  0:5 f ðxÞ ¼ 0:5 þ 2 ½1 þ 0:001ðx21 þ x22 Þ

2 2

[-10,10] [-10,10]

minf ¼ 0 minf ¼ 0

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

13

Table 7 Comparison results of function optimization. Functions

Algorithms

Value

Standard deviation

Optimization time

Success rate

Rastrigin

WDO APSO AGA WDO APSO AGA

5.716E-13 1.1986 6.5230 8.215E-16 0.0001 0.0101

1.806E-12 0.7418 5.2764 2.368E-15 0.0002 0.0125

0.0327 0.0278 0.0180 0.0328 0.0270 0.0211

100% 0% 0% 100% 100% 60%

Schaffer

Fig. 6. Convergence curves of each algorithm under different test functions: (a) Rastrigin, (b) Schaffer.

the best with increasing iteration times, and the convergence speed is significantly faster than those of the APSO and AGA. As Fig. 6 shows, close to the horizontal coordinates, the WDO algorithm has no fluctuation change with the increase in the number of iterations, which reflects its convergence stability and convergence reliability. In summary, the performance of the WDO is clearly better than that of the AGA and APSO. 4.2. Comparative analysis of evaluation results For practical applications, the comparison of the APSO and AGA shows that the process of the AGA is simple, but an individual step has no memory, the genetic operation is blind and has no direction, and the convergence time required in the application of the example is long. The principle of the APSO method is simple, easy to implement, and stable; however, it easily falls into local minima, and the application is not suitable for high-dimensional complex problems. The above research determined that the WDO algorithm has a good optimization accuracy and a very good optimization ability. To verify the combination of the WDO with the PP model in practical application, the critical index values of Table 2 are normalized, and the data are compared by using the WDO-PP, APSO-PP and AGA-PP models. The corresponding hierarchical intervals of three models are obtained. The results are shown in Table 8. The evaluation index values in Table 8 are normalized according to Formulas (1) and (2). The flood disaster RI and the corresponding flood disaster resilience level are obtained by bringing the

normalized data into the WDO-PP, APSO-PP and AGA-PP models, as shown in Table 9. Tables 8 and 9 show the following: 1) The overall flood disaster resilience level of the Hongxinglong Administration Bureau has been improved by the overall increase in the flood disaster resilience of each farm in each model. 2) In the evaluation of the flood disaster resilience levels of the Bawusan, Erjiuyi, Shuangyashan, Jiangchuan, Shuguang and Baoshan farms, the evaluation results are consistent compared with the other two models. 3) Combined with the actual situation, the WDO-PP model is more in line with the actual situation of Hongxinglong Administration Bureau. The key limiting factor of the flood disaster resilience of the Youyi farm is the shelter forest area ratio, and the weight of each index of flood disaster resilience is 0.709273. The change in the shelter forest area ratio of this farm is from 0.48% to 0.52%, which is higher than the average level of the whole shelter forest area ratio of 0.4% in the Hongxinglong Management Bureau. The flood disaster resilience evaluation result should be at a high level. The evaluation level of the APSO-PP method is IV, which is not consistent with the actual situation of the Youyi farm; thus, the results of the APSO-PP evaluation are not accurate. The key limiting factor of the Bawuer farm is the agricultural water use efficiency, which accounts for 0.626619 among all flood disaster resilience indexes. The agricultural water use efficiency is at a high level of 98.82%. Therefore, the level of flood disaster resilience of the farm should be good, the APSO-PP and AGA-PP evaluation levels are IV and III, respectively, and the actual situation of the Bawuer farm does not match. The key limiting factor of the Raohe farm flood disaster resilience is the

Table 8 Grade simulation interval of flood disaster resilience evaluation model. Level

APSO-PP Projection interval

AGA-PP Projection interval

WDO-PP Projection interval

I II III IV

2.9382 [1.5115, 2.9382) [1.0787, 1.5115) <1.0787

3.4231 [1.6873, 3.4231) [1.2569, 1.6873) <1.2569

0.9512 [0.4032, 0.9512) [0.2655, 0.4032) <0.2655

14

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

Table 9 The resilience index and corresponding grade trend of flood disasters in the farms of the Hongxinglong Administration under each model. Farm

Youyi Wujiuqi Bawuer Bawusan Raohe Erjiuyi Shuangyashan Jiangchuan Shuguang Beixing Hongqiling Baoshan

WDO-PP

Resilience trends

2002

2009

2016

0.6441 0.2295 0.904 0.3989 1.0461 1.8729 0.2457 2.7482 0.3868 0.3997 0.2541 2.485

0.6882 0.2575 0.9512 0.4149 1.0367 1.8744 0.2655 2.7951 0.4032 0.4045 0.2829 2.4689

0.704 0.2568 0.9375 0.4513 1.0415 1.8782 0.2724 2.8069 0.3825 0.4052 0.2821 2.573

II IV II/I/II III/II I I IV/III I III/II/III III/II IV/III I

APSO-PP

Resilience trends

2002

2009

2016

0.8809 1.2067 0.766 2.0628 1.3859 3.9712 0.5033 8.2572 1.3737 1.1594 1.0752 1.655

1.0529 1.671 1.0102 2.1986 1.7655 4.2243 0.6106 8.2783 1.3116 1.7062 1.25 1.7513

1.2249 1.8046 1.0687 2.2542 1.6832 4.2361 0.8077 8.2142 1.2651 1.2794 1.1396 2.1569

forest coverage rate, which accounts for 0.56426 in each flood disaster RI. The change in the forest coverage rate is from 31% to 29%, which is much higher than the average level of the forest coverage rate of 20% in the Hongxinglong Management Bureau, although it has decreased; therefore, the farm flood disaster resilience is better, the APSO-PP and AGA-PP evaluation level is level III, and this result does not meet the actual situation of the Raohe farm. As a result, the APSO-PP and AGA-PP evaluation methods are not accurate. 5. Conclusion (1) The flood disaster resilience system is a complex system covering the natural environment, human society, economic development and flood control technology dimensions. To construct an evaluation index system of the flood disaster resilience based on four dimensions in a complex system, the R clustering analysis method was adopted to classify the indicators within the same dimension to ensure that the screening index information was not repeated and covered all dimensions. Using factor analysis, the maximum index of information content in each category was selected, 15 optimal indexes were screened, and the original information of the primary evaluation index system was reflected by 34.88% of the preferred index. (2) In the optimization calculation of test function, the average value of the APSO and AGA are 1.22Eþ11 times larger than that of the WDO algorithm, the standard deviation is 8.45Eþ10 times larger than that of the WDO algorithm, and the success rate of optimization is WDO > APSO > AGA. Moreover, the convergence speed of the WDO algorithm is fast and the fitting effect is the best, showing adaptability, accuracy and good stability. The optimal projection direction of the PP model is searched by the WDO algorithm, which avoids the local minimum value of the optimization search process and improves the evaluation accuracy of the flood disaster resilience of the PP model. Compared with the evaluation results of the AGA-PP and APSO-PP models, the evaluation results of the WDO-PP model were more consistent with the important impact indicators of flood disaster resilience, i.e., forest coverage rate and agricultural water use efficiency, showing that the WDO-PP model more accurately reflects the actual situation. (3) The spaciotemporal change in flood disaster resilience of the Hongxinglong Administration Bureau is as follows: The law of spatial-temporal change is that it decreases gradually from the south, north, east and west toward the central inland area. The overall flood disaster resilience index of the study

IV/III III/II IV II III/II I IV I III III IV/III II

AGA-PP

Resilience trends

2002

2009

2016

1.2737 1.1149 1.0587 2.066 1.2631 1.6741 0.834 8.6292 1.4384 0.6201 2.9905 2.3448

1.6149 1.5204 1.3155 2.2561 1.4195 2.0126 1.0352 8.7313 1.5395 0.7997 3.5356 2.2607

1.8047 1.7424 1.4686 2.361 1.1013 2.2022 1.2359 8.9775 1.6664 0.9379 3.2159 3.5769

III/II IV/II IV/III II III/IV III/II IV I III IV II II/I

area has an increasing trend, of which 75% of the farm flood disaster resilience levels are levels I and II. During the study period, the flood disaster resilience index of two farms, Bawuer and Shuguang, was 0.90e0.95 and 0.38e0.41, respectively, and the flood disaster resilience level was basically consistent. However, the flood disaster resilience level of the Bawusan, Beixing, Shuangyashan and Hongqiling farms increased by one level, while the flood disaster resilience level of the remaining farms remained unchanged. The level of farm flood disaster resilience is mainly related to six indicators: forest coverage rate, paddy field coverage ratio, shelter forest area ratio, proportion of primary industry, agricultural water use efficiency and irrigation and drainage capacity. (4) The resilience intensity of regional flood disasters is an important and difficult problem that must be solved urgently in the field of disaster science. Flood data monitoring is difficult. However, with the construction and improvement of disaster monitoring network and large data platform, realtime monitoring data will be continuously obtained, which will lay a foundation for identifying better indicators, improving evaluation index systems and obtaining more accurate evaluation results in subsequent flood disaster resilience research. This study for the first time attempted to combine the WDO algorithm with the PP model, and although the success rate is relatively high, the search time is relatively long, With the extension of artificial intelligence technology, the following optimization algorithm with better stability and reliability will be further excavated to improve the operation efficiency. Furthermore, this study only carries out research on the scale of farms, although the research area will be expanded in the future and the existing results will be compared to further deepen the flood disaster resilience research. Acknowledgement This study is supported by the National Natural Science Foundation of China (No.51579044, No.41071053), National Science Fund for Distinguished Young Scholars (No.51825901), National Key R&D Program of China (No.2017YFC0406002), Natural Science Foundation of Heilongjiang Province (No.E2017007). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclepro.2019.118406.

D. Liu et al. / Journal of Cleaner Production 241 (2019) 118406

15

Appendix 1. Initial value matrix of 15 indicators for 12 farms

2

A2002

0:380 6 0:644 6 6 0:289 6 6 0:489 6 6 0:156 6 6 0:422 ¼6 6 0:311 6 6 0:467 6 6 0:511 6 6 0:333 6 4 0:133 0:333 2

A2009

6 6 6 6 6 6 6 6 6 ¼6 6 6 6 6 6 6 6 6 4

2

A2016

0:291 0:556 0:133 0:356 0:089 0:311 0:244 0:289 0:244 0:178 0:067 0:289

0:378 6 0:644 6 6 0:289 6 6 0:333 6 6 0:000 6 6 0:356 ¼6 6 0:356 6 6 0:289 6 6 0:356 6 6 0:311 6 4 0:156 0:356

0:140 0:139 0:426 0:413 0:702 0:069 0:963 0:069 0:259 0:945 0:415 0:000 0:167 0:151 0:426 0:441 0:660 0:094 0:924 0:078 0:291 0:958 0:472 0:061 0:170 0:068 0:439 0:454 0:660 0:101 0:624 0:082 0:190 0:958 0:472 0:092

0:180 0:192 0:171 0:342 0:185 0:487 0:009 0:554 0:216 0:033 0:415 0:801 0:254 0:234 0:095 0:342 0:357 0:491 0:011 0:682 0:216 0:024 0:578 0:854 0:476 0:260 0:148 0:566 0:427 0:522 0:034 0:773 0:336 0:013 0:506 1:000

0:451 0:808 0:227 0:281 0:042 0:964 0:000 0:916 0:692 0:007 0:073 0:964 0:489 0:355 0:248 0:318 0:065 1:000 0:059 0:895 0:708 0:008 0:090 0:990 0:493 0:398 0:256 0:329 0:067 0:964 0:123 0:934 0:603 0:008 0:090 0:861

0:009 0:000 0:027 0:075 0:211 0:057 0:029 0:022 0:054 0:031 0:050 0:030 0:069 0:026 0:063 0:258 0:188 0:184 0:031 0:022 0:055 0:031 0:105 0:017 0:069 0:072 0:288 0:311 0:196 0:172 0:073 0:046 0:064 0:031 0:108 0:899

0:091 0:300 0:442 0:378 0:714 0:585 0:000 0:169 0:055 0:287 0:581 0:448 0:137 0:316 0:395 0:449 0:862 0:645 0:078 0:290 0:202 0:382 0:597 0:157 0:186 0:379 0:471 0:636 0:994 0:582 0:131 0:414 0:287 0:474 0:602 0:663

0:060 0:154 0:228 0:290 0:253 0:404 0:005 0:271 0:037 0:018 0:368 0:376 0:208 0:373 0:406 0:493 0:708 0:608 0:164 0:446 0:214 0:341 0:627 0:316 0:362 0:449 0:388 0:682 0:872 0:713 0:325 0:534 0:345 0:379 0:572 0:777

0:127 0:060 0:067 0:139 0:261 0:181 0:058 0:197 0:049 0:174 0:269 0:244 0:140 0:112 0:291 0:285 0:231 0:326 0:139 0:356 0:327 0:480 0:301 0:273 0:074 0:064 0:038 0:049 0:001 0:000 0:103 0:038 0:058 0:094 0:018 0:045

0:595 0:991 0:746 0:685 0:773 0:692 0:485 0:509 0:000 0:470 0:734 0:714 0:507 0:538 0:400 0:433 0:652 0:552 0:265 0:731 0:336 0:603 0:581 0:544 0:577 0:340 0:342 0:470 0:507 0:671 0:671 0:710 0:438 0:628 0:449 0:795

0:968 0:978 0:926 0:945 0:990 0:933 0:967 0:918 0:873 1:000 0:952 0:900 0:835 0:813 0:679 0:772 0:834 0:726 0:757 0:733 0:525 0:856 0:730 0:655 0:619 0:548 0:579 0:580 0:603 0:605 0:605 0:398 0:028 0:738 0:377 0:475

0:135 0:797 0:303 0:375 0:627 0:633 0:791 0:977 0:111 0:695 0:722 0:879 0:690 0:720 0:880 0:590 0:780 0:780 0:810 0:460 0:770 0:810 0:720 0:770 0:024 0:397 0:291 0:314 0:677 0:258 0:435 0:269 0:357 0:235 0:396 0:220

0:243 0:110 0:051 0:537 0:126 0:426 0:265 0:562 0:000 0:187 0:017 0:000 0:331 0:884 0:113 0:170 0:393 0:339 0:299 0:024 0:000 0:777 0:079 0:000 0:824 0:951 0:589 0:182 0:703 0:679 0:132 0:466 0:120 0:987 0:144 0:103

0:829 0:950 0:963 0:954 0:954 0:972 0:262 1:000 0:921 0:950 0:947 1:000 0:899 0:924 0:807 0:929 0:971 0:942 0:000 0:959 0:801 0:706 0:976 0:978 0:938 0:927 0:889 0:946 0:971 0:935 0:131 0:961 0:873 0:499 0:966 0:977

0:025 0:085 0:053 0:190 0:038 0:303 0:000 0:285 0:049 0:010 0:317 0:404 0:097 0:138 0:046 0:223 0:358 0:366 0:001 0:346 0:050 0:020 0:417 0:250 0:157 0:155 0:074 0:361 0:440 0:357 0:013 0:413 0:094 0:012 0:360 0:557

3 0:417 0:190 7 7 0:405 7 7 0:417 7 7 0:083 7 7 0:714 7 7 0:000 7 7 0:714 7 7 0:071 7 7 0:012 7 7 0:857 5 0:143 0:714 0:440 0:417 0:464 0:179 0:714 0:000 0:714 0:048 0:012 1:000 0:143

3 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 5

3 0:738 0:464 7 7 0:429 7 7 0:464 7 7 0:179 7 7 0:714 7 7 0:000 7 7 0:714 7 7 0:036 7 7 0:012 7 7 1:000 5 0:143

References Note: The horizontal numbers in the matrix represent 12 farms in the order of Youyi, Wujiuqi, Bawuer, Bawusan, Raohe, Erjiuyi, Shuangyashan, Jiangchuan, Shuguang, Beixing, Hongqiling, and Baoshan: column vectors represent 15 indicators in the order of the annual average temperature, forest coverage rate, paddy field coverage ratio, shelter forest area ratio, cultural square per capita area, arable land per capita, per capita grain possession, number of vehicles per thousand people, proportion of primary industry, land economic density, agricultural investment ratio, proportion of investment in flood control, agricultural water use efficiency, per capita effective irrigated area, irrigation and drainage capacity. Matrix A2002, A2009, and A2016 represent 2002, 2009 and 2016, respectively.

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