Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen

Journal Pre-proof Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen Hai-Min Lyu, Wan-Huan Zhou, Shui-Long Shen, An-Nan Zhou

PII:

S2210-6707(20)30090-1

DOI:

https://doi.org/10.1016/j.scs.2020.102103

Reference:

SCS 102103

To appear in:

Sustainable Cities and Society

Received Date:

3 December 2019

Revised Date:

3 February 2020

Accepted Date:

13 February 2020

Please cite this article as: Lyu H-Min, Zhou W-Huan, Shen S-Long, Zhou A-Nan, Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen, Sustainable Cities and Society (2020), doi: https://doi.org/10.1016/j.scs.2020.102103

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Inundation risk assessment of metro system using AHP and TFN-AHP in Shenzhen

Hai-Min Lyu, PhD, Post-doctoral Fellow State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau S.A.R., China Email: [email protected]

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Wan-Huan Zhou*, PhD, Associate Professor State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macau S.A.R., China Email: [email protected]

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Shui-Long Shen*, PhD, Professor Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong 515063, China. Key Laboratory of Intelligent Manufacturing Technology (Shantou University), Ministry of Education, Shantou, Guangdong 515063, China Email: [email protected] Phone and Fax: (86)754-8650-4551

Highlights

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An-Nan Zhou, PhD, Associate Professor Civil and Infrastructure Engineering Discipline, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria 3001, Australia Email: [email protected]

Both AHP and TFN-AHP were incorporated into GIS to evaluate inundation risk;

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Inundation risk of metro system was evaluated based on regional risks along

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

metro lines;

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Comparation between AHP and TFN-AHP assessment results were analyzed;

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The results were validated by “4.11 flood event” in Shenzhen, China.

Abstract 1

Metro system is a vital transportation infrastructure, which has been inundated frequently during flood season (from May to September) in Shenzhen, China. This study incorporates the original analytic hierarchy process (AHP) and triangular fuzzy number-based AHP (TFN-AHP) into a geographic information system (GIS) to assess the inundation risk of the metro system in Shenzhen. The assessed results are verified by the 11 flood locations in the flood event that happened on April 11, 2019 (hereafter

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called the “4.11 flood event”). The risk indicators of the 11 flood locations derived

from the TFN-AHP are greater than those obtained from the AHP. Most of the metro

lines of the higher-risk and highest-risk match the regions with high inundation risks.

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The percentages of the higher-risk and highest-risk levels of the metro system

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obtained from TFN-AHP are greater than those from the AHP. The comparative result

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than the original AHP.

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indicates that the TFN-AHP method can assess the inundation risk more distinctively

Keywords: triangular fuzzy number, AHP, GIS, inundation risk, metro system,

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Shenzhen.

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1. Introduction

Flood is one of the most serious natural disasters that occur across the world. The

frequency of flood occurrence is often higher than that of other disasters, including typhoons, earthquakes, forest fires, heavy snow, and droughts (He et al., 2011; Atangana Njock et al., 2020). According to recent estimates, the economic loss caused 2

by floods accounts for 40% of the total loss attributed to all natural disasters (Xia et al., 2008). Frequent flood events have resulted in a worldwide increase in inundation risk (Werren et al., 2015; Wu et al., 2018, Lyu et al., 2019a, b, c). Because of the rapid urban development in flood-prone areas, the exposure of people to flood hazards is increasing, which leads to an increase in inundation risk. Rapid urban development has also led to the extensive construction of underground infrastructure. Metro system is

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one of the typical underground infrastructures that have been constructed to alleviate

traffic congestions, especially in mega cities in China (Zhou et al., 2013, 2018; Du et al., 2018; Lyu et al., 2020a, b). Metro stations are, hence, one of the vulnerable

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increase the inundation risk of a metro system.

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structures that are exposed in flood-prone areas. The exposed metro stations also

In recent years, many metropolitan cities, especially in coastal regions such as

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Guangzhou (Lyu et al., 2016, 2018), Shenzhen, Shanghai (Xu et al., 2019), Ningbo

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(Wang et al., 2019), Ganzhou (Xu et al., 2018), and Wuhan, have been flooded during the flood season from May to September (Wang et al., 2015; Lyu et al., 2016, 2019).

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These floods not only submerged buildings on the surface, but also inundated underground infrastructure, specifically metro stations. On May 10, 2016, the

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Changpan Station of the Guangzhou Metro was flooded (Lyu et al., 2016) causing eight deaths. From June 30 to July 5, 2018, southern China suffered from several severe flood events. Many metro stations, e.g., Wuhan Station, were submerged during this flood disaster (Lyu et al., 2019a).

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Shenzhen is a typical costal city, frequently suffered from flooding, typhoon, and tsunami. As an international economic centre, Shenzhen plays a critical role to connect the Guangdong province and Hong Kong during the development of Guangdong-Hong Kong-Macau great bay area. According to statistics (SYS, 2019), the gross national product of Shenzhen reached 2.5 million in 2018. Rapid economy leads to the urban construction, especially for metro lines. In recent years, frequently

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urban flooding not only submerged the surface buildings, but also inundated the

underground infrastructures in Shenzhen. On April 11, 2019, the Chegongmiao Station on line 1 of the Shenzhen Metro was flooded (XLN, 2019). This flood event (hereafter

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called the “4.11 flood event”) caused 11 deaths, and the metro line 1 was out of service

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for two hours. There are many such flood events affecting the underground metro stations. Therefore, it is essential to assess the inundation risk in these areas to mitigate

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the potential flooding of metro systems.

2. Literature review

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To reduce the damage caused by floods, assessing inundation risk is an urgent requirement in China. Generally, four types of approaches have been used to assess

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inundation risk, including (1) historical data-based method (Nott, 2006; Adikari et al., 2010), (2) index-based system (Yang et al., 2018; Lyu et al., 2018, 2019a), (3) scenario simulation approach (Yin et al., 2016; Tan et al., 2016, 2018; Gamse et al., 2018), and (4) geographic information system (GIS) and remote sensing (RS) techniques (Cai et al., 2019). Historical data-based methods are adopted to predict inundation risk by 4

analysing historical flood events (Nott, 2006). This method requires a large amount of data to be able to understand the temporal variations in the study area. The index-based system considers the inundation risk as an assessment structure, which includes several assessment factors. Risk indicators have crucial effects on the assessment results in the method of index-based system. The determination of risk indicators mainly depends on judgement of consultant experts based on their experiences, which may have

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subjectivities and uncertainties. These uncertainties lead to the assessment results obtained by an index-based system may suffer from an accuracy problem when

assessing the spatial distribution of inundation risk (Xiao et al., 2017). The scenario

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simulation approach is based on the scenario model, which is a quantitative prediction.

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Scenario-based inundation analysis is commonly used to predict inundation risk in a small region, whereas floods usually occur at the regional scale (Sampson et al., 2012),

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the application of this method is limited. GIS and RS techniques are useful tools to

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integrate different datasets, which can help provide a inundation risk map under different situations of urban growth (Kabenge et al., 2017).

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Although extensive efforts have been made to mitigate inundation risk, management difficulties still exist given the uncertainties with respect to such natural

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disasters. Multiple-criteria decision making (MCDM) methods and artificial intelligence techniques are widely used to solve the complex problems involved with uncertainties (Kubler et al., 2016; Souissi et al., 2019; Zhang et al., 2019). The analytical hierarchy process (AHP) proposed by Saaty (1977) is one of the most widely adopted approaches in MCDM (Saaty 2008). The main advantage of AHP lies in its 5

impartial and logical classification system, and the flexibility to integrate various assessment factors. However, it is difficult to avoid the bias that exists in human judgments and the pairwise comparisons of assessment factors. These shortcomings have motivated researchers to improve the traditional AHP to achieve the desired goal in a more efficient way. During the application of traditional AHP, as the pairwise comparison is

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conducted according to the decision makers’ opinions, the value that represents the

relative importance degree is subjective. The subjective importance degree has a bias,

which leads to uncertainties in the assessment weights. Therefore, the risk assessed by

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using AHP method inevitably has uncertainties. In risk assessment, the uncertainty of

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the AHP cannot be eliminated, but it can be mitigated. To improve the efficiency of the application, fuzzy numbers (e.g., interval, triangular, and trapezoidal fuzzy

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numbers) have been applied to the traditional AHP (Abhishek and Kumar, 2018; Lyu

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et al., 2019). In the fuzzy AHP (FAHP), the degree of relative importance is represented using a fuzzy number, rather than a crisp number, and this fuzzy number

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can reflect the uncertainty of risk as accurately as possible. The application of fuzzy numbers to the AHP is an efficient way to improve the traditional AHP method.

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As a practical popular method for dealing with fuzziness and uncertainty, the

FAHP is also a widely used method in risk assessment (Mardani et al., 2015). Generally, the fuzzy numbers include the interval fuzzy number, the triangular fuzzy number, and the trapezoidal fuzzy number. The interval/triangular/trapezoidal fuzzy numbers have their own corresponding FAHP. In the triangular fuzzy number-based 6

AHP (TFN-AHP), the medium value of the triangular fuzzy number represents the largest possibility of the assessment model, while the minimum and maximum values refer to the fuzzy degree corresponding to the largest possibility (Yang et al., 2013; Lyu et al., 2019). The TFN-AHP can assess the risk with the largest possibility to increase the accuracy of results. This is the reason that the TFN-AHP is selected in this study. During the application of the TFN-AHP method, the key is to determine the triangular

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fuzzy numbers that are used to express the degree of relative importance of the assessment factors.

GIS is an useful tool to visual the spatial distribution of inundation risk. The

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existing researches related to GIS mainly focused on the inundation risk of road

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network in urban area (Yin et al., 2016; Cai et al., 2019). There are few studies, which paid attention on the inundation risk of metro system. However, frequent flood events

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of metro lines arise the public attentions from academic and government (Lyu et al.,

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2019). In this respect, the GIS approach is applied to assess the inundation risk of metro system in this study. The usage of a combination of FAHP and GIS is an effective way

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to obtain the spatial distribution of inundation risk while considering the uncertainties. Based on the review (Yang et al., 2013; Kubler et al., 2016), to consider the

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uncertainties of inundation risk, the TFN-AHP were incorporated into GIS during the risk assessment procedure. The traditional AHP is compared to demonstrate the efficiency of the TFN-AHP. This study applied the TFN-AHP method combined with the GIS technique to assess the inundation risk of the metro system in Shenzhen. The objectives of the 7

study are to

(i) apply both the AHP and TFN-AHP methods incorporated into a GIS

to assess the inundation risk of the metro system in Shenzhen; (ii) compare the difference between the AHP and TFN-AHP results; (iii) analyse the spatial distribution of flood risk in Shenzhen as well as the metro system.

3. Study area and data collection

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3.1 Study area

Shenzhen is a major sub-provincial city located on the east bank of the Pearl

River estuary on the central coast of southern Guangdong province. It includes the

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Futian, Luohu, Yantian, Dapeng, Pingsha, Longgang, Longhua, Nansha, Bao’an, and

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Guangming districts, with an area of 1997 km2. Fig. 1 shows the administrative region and distribution of metro lines in Shenzhen. As shown in Fig. (1), Shenzhen is located

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between the longitudes 113°50′0″–114°40′0″E and latitude 22°40′0″N in the estuary

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of the Pearl River, and it is surrounded by Dongguan and Huizhou cities in the northeast and Hong Kong in the southeast. As a typical coastal city, Shenzhen city is

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surrounded by the Shenzhen, Dapeng, and Daya bays. With the rapid development of the Hong Kong–Zhuhai–Macau big bay, an increasing number of metro lines have

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been constructed in Shenzhen. As shown in Fig. (1), the metro lines are densely distributed in Futian and Luohu districts. At present, eight metro lines are in operation, and five are under construction. The planned metro system will consist of 16 lines in 2020. Risks in the metro system exist both in metro line operation and construction. In the “4.11 flood event”, Chegongmiao Station of line 1 was flooded (see Fig. 2). This 8

flood event caused 11 deaths. The flood locations are presented in Fig. 2.

3.2 Rainfall data acquisition and processing The data of the study area were collected from websites and the local government. Rainfall plays a crucial role during a flood event. To investigate the rainfall characteristics in Shenzhen, annual rainfall (AR) data were collected from the

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Statistical Yearbook of Shenzhen (SYS, 2019). Fig. 2 shows the variation of AR from

2000 to 2018 in Shenzhen. The average value of AR from 2000 to 2018 is ARave = 1915 mm. The ratio of rainfall excess to the average value is obtained using the

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difference between ARi and ARave. This ratio reflects the variation of each year’s

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rainfall and the average AR. A ratio with negative value means that the rainfall is less than the average AR. As shown in Fig. 2, the highest AR occurred in 2000 and 2008.

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After 2011, the rainfall presents an increase trend, which increases the inundation risk.

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Moreover, extreme rainstorm is perhaps the most important driving force of flood disasters. Rainstorm is defined as a daily rainfall (DR) within 24 h that is in

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excess of 50 mm. To reflect the characteristic of a rainstorm, the rainfalls with DR in excess of 100, 150, and 200 mm from May 31, 2018 to May 31, 2019 were collected

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from the Meteorological Bureau of Shenzhen Municipality. Fig. 3 shows the spatial distribution of rainfall characteristics. Fig. 3(a) is the spatial distribution of the DR in excess of 100 mm. Fig. 3(b) is the spatial distribution of the DR in excess of 150 mm. Fig. 3(c) is the spatial distribution of the DR in excess of 200 mm. As shown in Fig. 3, the rainstorm in the south region is generally higher than that in the north region. The 9

spatial distribution of rainfall data is obtained using Kriging interpolation in the GIS. The basic theory of the Kriging interpolation is expressed using Eq. (1).

X (i)  a(i)  b(i)

(1)

where i is the spatial coordinate expressed in latitude and longitude; X(i) is the variable in the coordinate i; X(i) is composed by the trend value a(i), and the random deviation b(i). To achieve a precise assessment result, all the risk factors were

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normalized before overlay analysis. Based on the spatial distribution of each factor, the max-min normalised method in GIS is adopted, as described in Eq. (2).

mi max  mij mi max  mi min

(2)

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xij 

where xij is the normalized value; mimax and mimin are the maximum and minimum

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values; mij is the original value. The other data related to inundation risk are obtained

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be presented here.

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similarly. Given the restriction of length for this paper, the detailed processes will not

4. Methods and application

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4.1 Methods in the study

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4.1.1 AHP

The AHP method divides a complex problem into a structure with several layers,

e.g., object layer, index layer, and sub-index layer. In risk assessment, the risk is generally regarded as the object layer. The index and sub-index layers include several assessment factors. Numbers ranging from 1 to 9 and their reciprocals are used to 10

represent the relative importance of the assessment factors to risk in the pairwise comparison. If factor A is significantly more important than factor B for the risk, the relative importance degree of factor A to B can be reflected using number 9. Conversely, the importance degree of factor B to A can be expressed using the reciprocal number 1/9. The degrees of relative importance of the assessment factors are adopted to establish a consistent judgment matrix. Finally, the mathematical

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theory is applied to calculate the vector corresponding to the largest eigenvalue of the

judgment matrix. The calculated vector represents the weights of the assessment

factors, and it is used to quantify the effects of the factors to risk. The verification of

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described in the literature (Lyu et al., 2019a).

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the consistence of the judgment matrix and the weight calculation process are

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4.1.2 TFN-AHP

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The TFN-AHP is based on the application of a triangular fuzzy number in the traditional AHP. Instead of a crisp number, the TFN-AHP uses a triangular fuzzy

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number to express the importance degree between assessment factors. A triangular fuzzy number is defined as P = (l, m, μ) (l ≤ m ≤ μ), in which the parameters l and μ

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are the minimal and maximum values, respectively, while m is the most likely risk value. Parameters l and μ determine the interval fuzzy degree of the most probable risk value m. The meaning of the triangular fuzzy number is presented in Appendix. Instead of crisp numbers, the TFN-AHP uses the triangular fuzzy numbers in the judgment matrix (see Appendix). The application of the TFN-AHP is to calibrate the 11

connection degree between two triangular fuzzy numbers. Fig. 4 shows the membership between triangular fuzzy number P1 and P2. The key problem is to calibrate the intersection distance μ(d) between P1 and P2. Eq. (3) describes the membership function of P1 and P2, which is adopted to calibrate the parameter μ(d). The detailed information of the calibration procedure is presented in Appendix.

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4.1.3 Incorporation of AHP or TFN-AHP into GIS

(3)

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 1 (m1  m2 )   ( d )  0 (l2  u1 )  l2  u1  (otherwise)  (m1  u1 )  (m2  l2 )

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The inundation risk is defined as the combination of hazard, exposure and vulnerability indices. Each indictor consists of different risk factors. The inundation

n

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risk can be redefined by Eq. (4) (Liu et al. 2016): n

n

j 1

k 1

FR  wH ( wi H i ) Hazard  wE ( w j E j ) Exposure  wV ( wkVk )Vu ln erability

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i 1

where wH, wE, and wV are the weights of hazard index, exposure index, and

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vulnerability index, respectively; wi, wj and wk are the weights of different risk

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factors. The weights of assessment factors can be calculated by AHP and TFN-AHP. Based on the normalized factors and their corresponding weights, the inundation risk can be obtained according to Eq. (3) using the tool of grid calculation in GIS.

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(4)

4.2 Assessment procedure Fig. 5 shows the flowchart of the inundation risk assessment for metro system. The assessment process including three major phases. The first one establishes the assessment structure. The inundation risk is defined as the combination of hazard, exposure, and vulnerability, and thus, the assessment structure is established with the corresponding indices. Each index consists of different assessment factors. Based on

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the established assessment structure, the factors of each index are processed in the second phase. All the data in the assessment structure are reproduced in a GIS. In the third phase, the weights of the assessment factors are calculated using the AHP and

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TFN-AHP methods. The regional inundation risk is obtained by the calibration of the

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processed data and the corresponding weights in the GIS. Based on the regional

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along the metro lines.

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inundation risk, the risk level of the metro system can be obtained extracting the risk

4.2.1 Hazard index

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The hazard index is the internal driving factor that induces the inundation risk. The hazard index includes the factors of rainstorm with DR > 200 mm (H1), rainstorm

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with DR > 150 mm (H2), rainstorm with DR > 100 mm (H3), maximum daily rainfall (H4), and annual average rainfall (H5). The spatial distributions of factors H1, H2, and H3 are shown in Fig. 3. The data processing of the factors in the hazard index was introduced above.

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4.2.2 Exposure index The exposure index includes the topographic characteristics, including elevation (E1) and slope (E2). The elevation and slope were generated from a digital elevation model (DEM) using the 3D Analyst tools in the GIS. A region with relative low elevation and steep slope is prone to flood. A flood disaster is related to the distribution of the drainage system. The drainage indices of density and proximity

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were designed to delineate floods from the river network. The characteristics of the river network are reflected using river density (E4) and proximity (E5). The river

density indicates the length of the river network per unit area, whereas river proximity

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refers to the distance to the closest river. The river density was computed from the

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Line Density function in the GIS using a 2 km radius. The river proximity was obtained using the Multiple Buffer operator in the GIS. Moreover, the exits of metro

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stations are vulnerable to flood. The buffer of exit points (E3) in a metro station

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increase the inundation risk of the metro system. The area of the buffer calculation

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within 30 m range based on the exit point is considered as an exposure index.

4.2.3 Vulnerability index

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The vulnerability index refers to the resistance of the disaster bearing body. The

vulnerability index includes the following factors: land use type (V1), population density (V2), metro line density (V3) and proximity (V4), and road network density (V5) and proximity (V6). Different land use types have different inundation risks. A region with high population density has a high inundation risk. An extreme rainstorm easily 14

floods roads, resulting in traffic congestion. Thus, the road network is considered as an assessment factor. The characteristics of the road network are reflected using the road network density and proximity. The road network density reflects the length of the road network per unit area, whereas the road network proximity refers to the distance to the closest road (Chen et al., 2013). The metro line density and proximity are used to reflect the effects of the metro system. As with the river network, the

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density and proximity of the road network and metro system are obtained in the GIS.

4.3 Weight calibration

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4.3.1 AHP weight

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Based on the AHP method, the judgment matrix of pairwise comparison in the index layer is shown in Eq. (3). The consistency ratio (CR) of the matrix Findex is

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0.0462 (less than 0.1), indicating that the judgment matrix is consistent. According to

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the consistent judgment matrix, the weights of the hazard, exposure, and vulnerability indices can be calibrated. Similarly, the weights of the assessment factors of each

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index layer can be obtained. Table 1 tabulates the weights of assessment factors

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calibrated from the AHP and TFN-AHP methods.

Findex

  H  E  V

H

E

1

2

0.5

1

2

2

V   0.5  0.5   1 

4.3.2 TFN-AHP weight Each the elements in the consistent judgment matrix can be replaced using a 15

(3)

triangular fuzzy number. The judgment matrix of the index layer is used as an example to illustrate the weight calibration process using the TFN-AHP method. Table 2 lists the triangular fuzzy judgment matrix of the assessment indices. Based on the fuzzy judgment matrix, the fuzzy synthetic extent (Pi) can be calculated. As listed in Table 2, the fuzzy synthetic extent (Pi) of each index is consisted by a triangular fuzzy number. Each fuzzy synthetic extent Pi is plotted as a triangle. Fig. 6 shows the

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triangular fuzzy membership between hazard, exposure, and vulnerability. Tables 3 to 5 present the triangular fuzzy judgment matrixes of the assessment factors in hazard,

exposure and vulnerability indices. Based on the established triangular fuzzy

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judgment matrix, the triangular fuzzy membership of the assessment factors in each

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index layer can be obtained.

The membership between hazard and exposure can be calculated as follows:

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 ( PH  PE )  1 ;  ( PE  PH )  0.6905 ;

The membership between exposure and vulnerability can be calculated as follows:

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 ( PE  PV )  0.4920 ;  ( PV  PE )  1 ;

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The membership between hazard and vulnerability can be calculated as follows:  ( PH  PV )  0.8347 ;  ( PV  PH )  1 ;

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According to Eq. (7), the original weight of the assessment indices can be obtained as follows:

w0  min   ( Pi  Pk )  (0.6905,0.4920,0.8347)

After normalisation, the weights of hazard, exposure, and vulnerability can be obtained as follows: 16

wb  (0.3423,0.2439,0.4138) . Similarly, the fuzzy synthetic extent and fuzzy weights of the assessment factors can be calculated using the TFN-AHP method.

4.4 Results and validation 4.4.1 Risk level of assessment indices

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Based on the normalised factors and their corresponding weights listed in Table 1, the risk level of each index can be obtained using the overlay analysis in the GIS

(see Fig. 5). Fig. 7 shows the spatial distribution of assessment indices. Fig. 7(a) is the

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spatial distribution of the hazard risk level. As shown in Fig. 7(a), the hazard levels in

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the Luohu and Yantian districts are higher than those in other districts. Fig. 7(b) depicts the spatial distribution of the exposure risk level. As shown in Fig. 7(b), the

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high risk is mainly distributed in the Luohu and Yantian districts and the western

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regions. The distribution of the metro system increased the exposure risk in the Futian district. Fig. 7(c) is the spatial distribution of the vulnerability risk level. As shown in

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Fig. 7(c), the Futian, Nansha, Bao’an, Luohu, Longgang, and Longhua districts have higher vulnerability risks than other regions. The high vulnerability risk is mainly

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located in the region with dense distribution of road network and metro lines.

4.4.2 Regional inundation risk According to the results of assessment indices and their corresponding weights obtained from the AHP and TFN-AHP methods (see Table 1), the results of regional 17

inundation risk can be obtained. Fig. 8 shows the spatial distribution of regional inundation risk obtained from the AHP and TFN-AHP methods. The regional risk level is reflected from high to low using the risk indicator from 0 to 1. Fig. 8(a) shows the spatial distribution of the regional inundation risk obtained from the AHP and Fig. 8(b) depicts that obtained from the TFN-AHP method. The spatial distribution of regional inundation risk from the AHP is similar to that from the TFN-AHP. As shown

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in Fig. 8, the high inundation risk mainly distributed in the Futian, Luohu and Yantian

districts. In the “4.11 flood event”, 11 people lost their lives. The flood locations in

“4.11 flood event” are presented in Fig. (8). These flood locations mainly distributed

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in Luohu and Futian districts with high inundation risk. To compare the difference

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between the AHP and TFN-AHP methods, the risk indicators of the flood locations were extracted. Fig. 9 shows the variation in the risk indicators between the AHP and

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TFN-AHP methods. The risk indictors of points 1 to 3 located in Futian district are

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generally less than points 4 to 11 (except for point 7) located in Luohu district. As reported in “4.11 flood event”, the average water depth was 39.7 and 65 mm in Futian

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and Luohu districts. The results show that the area with a larger risk indictor has a higher risk to inundation. Moreover, the risk indicators obtained from the AHP have

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lower values than those derived from the TFN-AHP. The comparison indicates that the TFN-AHP method can provide a more distinctive inundation risk than the AHP. The adoption of triangular fuzzy numbers in the AHP can reduce the uncertainties of inundation risk assessment.

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4.4.3 Inundation risk of metro system Based on the result of the regional inundation risk, the risk of metro system can be extracted. The inundation risk along the metro line with the range 500 m is used to reflect the inundation risk of metro system. Fig. 10 shows the inundation risk of metro system. The inundation risk of metro system is classified into five levels. Fig. 10(a) is the spatial distribution of inundation risk for metro system obtained from AHP

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method, and Fig. 10(b) is obtained from TFN-AHP method. The inundation risk of

metro system from AHP is similar with that from the TFN-AHP method. As mentioned above, the Chegongmiao Station is inundated in “4.11 flood event”. The

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inundation risk of the Chegongmiao Station presents the higher-risk both from AHP

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and TFN-AHP methods. The risk indictor of Chegongmiao Station is 0.7759 from AHP method, and 0.8232 from TFN-AHP method. The risk indictor from TFN-AHP

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is larger than that from AHP, indicating that the TFN-AHP method can yield a more

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distinctive inundation risk of metro system.

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5. Discussion

5.1 Efficiency and limitation

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According to the assessment results, the Luohu, Futian, and Yantian districts

with dense distribution of metro lines were assessed at high risk level. During assessment procedure, the weights of assessment indicators have critical effects on assessed result. In previous study, rainfall is considered as the most important factors, which induced inundation risk (Wang et al., 2015). Rainfall is the source of risk, 19

whereas the vulnerability of metro system determines the response of rainfall. This study paid more attention on the vulnerability of metro system, which is considered as the most important on inundation risk. In the assessment system, the vulnerability index has the largest weight (see Table 1), which contributes the spatial distribution of high inundation risk mainly distributed in the region with dense metro lines. During the application of AHP and TFN-AHP methods, the AHP weight and

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TFN-based weight of assessment factors were incorporated into GIS using grid

calculation tool. The AHP weight is usually a single value, which may exist bias. While, the FTN-based weight is a triangular fuzzy number, which can reduce the

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uncertainties during risk assessment. In the TFN-AHP method, the fuzzy synthetic

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extent describes the importance of factors on risk using a triangular fuzzy number. To demonstrate the efficiency of the TFN-AHP approach, the percentages of different risk

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levels of the AHP and TFN-AHP results are obtained. Figure 11 shows the percentage

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of the different inundation risk levels for the metro system from AHP and TFN-AHP. The highest-risk level from the AHP method is 3.73%, and from the TFN-AHP method

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is 5.16% (Fig. 11). Additionally, the higher-risk level from the AHP method is 19.89%, and from the TFN-AHP is 23.98%. The percentages of the higher-risk and highest-risk

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levels from TFN-AHP are greater than AHP, which indicates that the TFN-AHP can capture more high-risk levels for a metro system. In the TFN-AHP method, the influence of a factor on inundation risk is reflected using a triangular fuzzy number. During the application of the TFN-AHP method, it is difficult to determine the triangular fuzzy numbers. The viewpoints from consulted 20

experts are adopted to determine the triangular fuzzy numbers, which are generally subjective. Therefore, the assessment results from the TFN-AHP still have inevitable uncertainties. Although the TFN-AHP has its limitations, the results obtained from this method provide supportive guidelines to help local governments manage the inundation risk of metro systems.

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5.2 Reflections and suggestions

In recent years, the pear delta region has experienced strong economic and

population growth. According to recent estimates, 120 million people are expected to

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live in the pear delta region, which is slowly emerging into a mega city surrounded by

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Hong Kong, Shenzhen, and Guangzhou. Shenzhen is a typical bay/delta area, which is experiencing an increase in inundation risk due to rainstorms, intense typhoons, and

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extreme weather events. In Shenzhen, 64% of the agricultural land had been

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transformed to industrial and residential areas from 1979 to 2010 (Chan et al., 2012). Rapid urbanization also causes an increase in inundation risk, because many residents

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and businesses are exposed to flood disasters. Flood awareness needs to be raised to help deal with inundation risks in a sustainable way. Several researches have indicated

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that it will be difficult to mitigate inundation risk if people rely only on flood defence structures. Approaches such as ‘living with rivers’ and ‘making space for water’ not only provide space for storing flood water, but also conserve natural habitats (Johnson et al., 2007). Policy makers need to establish flood management policies, and local governments should avoid construction in inundation-prone regions unless flood 21

countermeasures have been conducted. Growing inundation risk must be integrated into urban and rural planning, because strategic development plans that consider inundation risk could help create resilient cities.

6. Conclusions This case study adopts the AHP and TFN-AHP incorporated into GIS to assess the

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inundation risk of the metro system in Shenzhen. The major conclusions are summarised as follows:

(1) Both the AHP and TFN-AHP were incorporated into GIS to assess the inundation

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risk in Shenzhen. The results showed that the high-risk regions are mainly located in

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the Luohu, Futian, and Yantian districts, where the metro lines are distributed intensively. The high-risk of the regional flood risk increase the inundation risk of

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the metro system in Shenzhen.

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(2) The assessment results were verified by the “4.11 flood event”. There were 11 flood points located in the Luohu and Futian districts. The risk indicators of these 11 flood

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locations using the TFN-AHP method were larger than those obtained using the AHP method. The comparison indicates the efficiency of TFN-AHP method.

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(3) The inundation risk of the metro system was also assessed using the risk along metro lines. The results showed that most of the higher-risk and highest-risk metro lines are located in the region with high risk. The percentages of higher-risk and highest-risk obtained from the TFN-AHP were greater than those obtained from the AHP method. 22

(4) The Chegongmiao station in line 1 was inundated during the “4.11 flood event”, which station was assessed to be at higher-risk, with a risk indicator of 0.7759 from the AHP method and 0.8232 from the TFN-AHP method. The comparison between the AHP and TFN-AHP results showed that the TFN-AHP can achieve more distinctive inundation risk values compared to the original AHP. The assessed

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results provide a suggestion for local government to prevent flood disasters.

Declaration of interests

The authors declare that they have no known competing financial interests or personal

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-p

relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

Development

Fund,

Macau and

SAR

the

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FDCT/0035/2019/A1)

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The research work described herein was funded by the Science and Technology (File

no.

University

SKL-IOTSC-2018-2020 of

Macau

Research

and Fund

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(MYRG2018-00173-FST). These financial supports are gratefully acknowledged.

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Appendix: Introduction of TFN-AHP Table A1 presents the meaning of the triangular fuzzy number. Table A1 Linguistic variables and triangular fuzzy numbers with quantitative scales Linguistic terms

Fuzzy number

Triangular fuzzy scale

Reciprocal fuzzy number

Equally important

1

(1,1,1)

(1,1,1)

Almost equal important

1`

(1,1,3)

(1/3,1,1)

23

triangular

Intermediate value

2`

(1,2,4)

(1/4,1/2,1)

Moderately more important

3`

(1,3,5)

(1/5,1/3,1)

Intermediate value

4`

(2,4,6)

(1/6,1/4,1/2)

Strongly more important

5`

(3,5,7)

(1/7,1/5,1/3)

Intermediate value

6`

(4,6,8)

(1/8,1/6,1/4)

Very strong more important

7`

(5,7,9)

(1/9,1/7,1/5)

Intermediate value

8`

(6,8,10)

(1/10,1/8,1/6)

Extremely more important

9`

(7,9,11)

(1/11,1/9,1/7)

The TFN-AHP uses the triangular fuzzy numbers in the judgment matrix. Eq. (A1)

Pn   lij , mij , nij  nn

l1i , m1i , 1i 

l1n , m1n , 1n  

 l2 n , m2 n , 2 n      lin , min , in      1,1,1  

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l2i , m2i , 2i 

re

1,1,1

 1 1 1 ,   ,  in min lin 

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 1,1,1 l12 , m12 , 12    1 , 1 , 1  1,1,1  m l    12 12 12      1 1 1  1 1 1  , , , ,   m l   m l  1i 1i 1i  2i 2i 2i        1 , 1 , 1   1 , 1 , 1   m l   m l    1n 1n 1n   2 n 2 n 2 n 

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describes a judgment matrix with triangular fuzzy numbers.

(A1)

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where Pn is the triangular fuzzy judgment matrix. During the application of the TFN-AHP, it is crucial to establish the triangular fuzzy

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judgment matrix. Based on the established triangular fuzzy judgment matrix Fn, the

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next step is to calibrate the fuzzy synthetic extent Pk using Eq. (A2),  n n  Pk  Si   Pij   i 1 j 1 

1

(A2)

where Pij is the triangular fuzzy number of factor i to factor j; Si is the sum of the triangular fuzzy numbers, which can be calculated using Eq. (A3),

24

 n Si    l j ,  j 1

 mj , j 1

n

u j 1

j

  

(A3)

n

 P i 1 j 1

ij

can be calculated using Eq. (A4), n

n

 P i 1 j 1

 n n  Therefore,   Pij   i 1 j 1 

ij

 n n    lij ,  i 1 j 1

n

n

n

n

 m ,  u i 1 j 1

ij

ij

i 1 j 1

  

(A4)

1

can be calculated using Eq. (A5),

    1 1 , n n ,  Pij    n n   i 1 j 1    uij  mij i 1 j 1  i 1 j 1 n

1

n

     

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n

n

1

n

n

 l i 1 j 1

ij

(A5)

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The key is to calibrate the parameter μ(d) between to triangular fuzzy numbers in the application of the TFN-AHP. The parameter μ(d) can be described using Eq. (A6),

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 (di )  min   ( Pi  Pk ) (k  1, 2,3,......n, k  i)

(A6)

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The weight vector of the assessment factors in one layer can be obtained using Eq. (A7),

na

w0    (d1 ),  (d2 ),

 (d n ) 

T

(A7)

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The normalised weight vector can be expressed using Eq. (A8),

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where  (di )' 

 (di )

, and

n

  (d i 1

wb    (d1 )' ,  (d2 )' ,

i

)

n

  (d )  1 . '

i 1

i

25

 (d n )' 

T

(A8)

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Doi:

List of Figures and Tables

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Fig.1 Distribution of metro lines in operation and planned in Shenzhen

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Fig. 2 Annual rainfall from 2000 to 2018 in Shenzhen

35

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Fig. 3 Spatial distribution of rainfall characteristics in Shenzhen: (a) DR > 100 (mm);

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(b) DR > 150 (mm); (c) DR > 200 (mm)

36

37

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Fig. 4 Triangular membership between P1 and P2 (P1>P2)

Fig. 5 Flowchart of the inundation risk assessment for metro system in Shenzhen

38

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Fig. 6 Triangular fuzzy membership of assessment indices: (a) hazard, (b) exposure,

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(c) vulnerability

39

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Fig. 7 Spatial distribution of risk level for assessment indices: (a) hazard risk level, (b)

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exposure risk level, (c) vulnerability risk level

40

41

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Fig. 8 Spatial distribution of regional inundation risk: (a) AHP result, (b) TFN-AHP

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na

lP

result

42

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Fig. 9 Risk indicators of flood locations obtained from AHP and TFN-AHP

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Fig. 10 Inundation risk of metro system: (a) AHP result, (b) TFN-AHP result

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Fig. 11 Percentages of the different inundation risk levels for metro systems: (a) AHP results, (b) TFN-AHP results

44

45

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Table 1 Weights of assessment factors calibrated from AHP and TFN-AHP

Hi

Ej

Vk

AHP (wa)

0.310

0.197

0.493

Fuzzy synthetic extent (Pi)

(0.125,0.333,0.889)

(0.083,0.190,0.444)

(0.167,0.476,1.333)

TFN-AHP

(wb)

0.342

0.244

0.414

Factor

AHP (wa)

Fuzzy synthetic extent (Pi)

TFN-AHP

H1

0.320

(0.085,0.303,0.900)

0.259

H2

0.302

(0.085,0.257,0.847)

0.244

H3

0.211

(0.076,0.248,0.741)

0.239

H4

0.095

(0.044,0.108,0.397)

0.160

H5

0.072

(0.031,0.066,0.228)

0.098

E1

0.306

(0.085,0.289,0.896)

0.254

E2

0.324

E3

0.186

E4

0.111

E5

0.073

V1

0.261

V2

0.227

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Index

Sub-index layer

V3 V4

0.263

(0.060,0.202,0.633)

0.216

(0.045,0.120,0.422)

0.175

(0.031,0.070,0.237)

0.092

(0.064,0.228,0.729)

0.204

(0.056,0.218,0.696)

0.200

0.183

(0.058,0.205,0.596)

0.195

0.166

(0.082,0.177,0.596)

0.186

0.099

(0.041,0.122,0.348)

0.153

0.064

(0.021,0.051,0.141)

0.062

re

V5

Jo

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na

lP

V6

46

(wb)

(0.103,0.318,0.896)

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Index layer

Table 2 Judgment matrix with triangular fuzzy numbers for assessment indices Index

H

E

V

Sum of triangular fuzzy numbers (Si)

H

(1,1,1)

(1,2,4)

(0.25,0.5,1)

(2.5,3.5,6)

(0.125,0.333,0.889)

E

(0.25,0.5,1)

(1,1,1)

(0.25,0.5,1)

(1.5,2,3)

(0.083,0.190,0.444)

V

(1,2,4)

(1,2,4)

(1,1,1)

(3,5,9)

(0.167,0.476,1.333)

Jo

ur

na

lP

re

-p

ro of

Fuzzy synthetic extent (Pi)

47

Table 3 Judgment matrix with triangular fuzzy numbers for hazard index

Hi

H1

H2

H3

H4

H5

Sum of triangular fuzzy numbers (Si)

H1

(1,1,1)

(1,1,1)

(1,2,4)

(1,3,5)

(2,4,6)

(5,11,17)

(0.085,0.303,0.900)

H2

(1,1,1)

(1,1,1)

(1,2,4)

(1,3,5)

(1,3,5)

(5,10,16)

(0.085,0.257,0.847)

H3

(0.25,0.5,1)

(0.25,0.5,1)

(1,1,1)

(2,4,6)

(1,3,5)

(4.5,9,14)

(0.076,0.248,0.741)

H4

(0.2,0.33,1)

(0.2,0.33,1)

(0.17,0.25,0.5)

(1,1,1)

(1,2,4)

(2.57,3.91,7.5)

(0.044,0.108,0.397)

H5

(0.17,0.25,0.5)

(0.2,0.33,1)

(0.2,0.33,1)

(0.25,0.5,1)

(1,1,1)

(1.82,2.41,4.5)

(0.031,0.066,0.228)

Jo

ur

na

lP

re

-p

ro of

Fuzzy synthetic extent (Pi)

48

Table 4 Judgment matrix with triangular fuzzy numbers for exposure index

Ej

E1

E2

E3

E4

E5

Sum of triangular fuzzy numbers (Si)

E1

(1,1,1)

(1,1,1)

(1,2,4)

(1,3,5)

(1,3,5)

(5,10,17)

(0.085,0.289,0.896)

E2

(1,1,1)

(1,1,1)

(1,2,4)

(1,3,5)

(2,4,6)

(6,11,17)

(0.103,0.318,0.896)

E3

(0.25,0.5,1)

(0.25,0.5,1)

(1,1,1)

(1,2,4)

(1,3,5)

(3.5,7,12)

(0.060,0.202,0.633)

E4

(0.2,0.33,1)

(0.2,0.33,1)

(0.25,0.5,1)

(1,1,1)

(1,2,4)

(2.65,4.16,8)

(0.045,0.120,0.422)

E5

(0.2,0.33,1)

(0.17,0.25,0.5)

(0.2,0.33,1)

(0.25,0.5,1)

(1,1,1)

(1.82,2.41,4.5)

(0.031,0.070,0.237)

Jo

ur

na

lP

re

-p

ro of

Fuzzy synthetic extent (Pi)

49

Table 5 Judgment matrix with triangular fuzzy numbers for vulnerability index

V1

V2

V3

V4

V5

V6

(1,1,1)

(1,2,4)

(1,3,5)

(1,2,4)

(1,2,4)

(1,2, 4)

(6,12,22)

(0.064,0.228,0.7 29)

(0.25,0.5, 1)

(1,1,1)

(1,2,4)

(1,3,5)

(1,3,5)

(1,2, 4)

(5.25,11.5,21 )

(0.056,0.218,0.6 96)

(0.2,0.33, 1)

(0.25,0.5, 1)

(1,1,1)

(1,2,4)

(1,3,5)

(2,4, 6)

(5.45,10.83,1 8)

(0.058,0.205,0.5 96)

(0.25,0.5, 1)

(0.2,0.33, 1)

(0.25,0.5,1)

(1,1,1)

(2,4,6)

(4,6, 8)

(7.7,9.33,18)

(0.082,0.177,0.5 96)

(0.25,0.5, 1)

(0.2,0.33, 1)

(0.2,0.33,1)

(0.17,0.25,0. 5)

(1,1,1)

(2,4, 6)

(3.82,6.41,10. 5)

(0.041,0.122,0.3 48)

(0.25,0.5, 1)

(0.25,0.5, 1)

(0.17,0.25,0 .5)

(0.13,0.17,0. 25)

(0.17,0 .25,0.5 )

(1,1, 1)

(1.97,2.67,4.2 5)

(0.021,0.051,0.1 41)

V

V 4

V 5

V 6

lP

3

na

V

ur

2

Jo

V

re

1

-p

k

50

Fuzzy synthetic extent (Pi)

ro of

V

Sum of triangular fuzzy numbers (Si)