Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach

Science of the Total Environment 626 (2018) 1012–1025

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Flood risk assessment in metro systems of mega-cities using a GIS-based modeling approach Hai-Min Lyu a,b, Wen-Juan Sun c,⁎⁎, Shui-Long Shen a,b,⁎, Arul Arulrajah d a

State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration (CISSE), Shanghai Jiao Tong University, Shanghai 200240, China Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA 18015, USA d Department of Civil and Construction Engineering, Swinburne University of Technology, Hawthorn, Victoria 3122, Australia b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Regional flood risk level was evaluated using both AHP and I-AHP methods. • Flood risk level of metro system was evaluated based on flood risks within 500 m range from metro lines. • Comparative results between AHP and I-AHP assessment results were analyzed. • Results were validated using the observed flood hazards on May 10, 2016, in Guangzhou, China.

a r t i c l e

i n f o

Article history: Received 14 November 2017 Received in revised form 30 December 2017 Accepted 15 January 2018 Available online 19 February 2018 Editor: R Ludwig Keywords: Flood risk assessment Metro system I-AHP GIS

a b s t r a c t Metro system is a vital component of mass transportation infrastructure, providing crucial social and economic service in urban area. Flood events may cause functional disruptions to metro systems; therefore, a better understanding of their vulnerability would enhance their resilience. A comparative study of flood risk in metro systems is presented using the analytic hierarchy process (AHP) and the interval AHP (I-AHP) methods. The flood risk in the Guangzhou metro system is evaluated according to recorded data. Evaluated results are validated using the flood event occurred in Guangzhou on May 10, 2016 (hereinafter called “May 10th event”), which inundated several metro stations. The flood risk is assessed within a range of 500 m around the metro line. The results show that N50% of metro lines are highly exposed to flood risk, indicating that the Guangzhou metro system is vulnerable to flood events. Comparisons between results from AHP and I-AHP show that the latter yields a wider range of high flooding risk than the former. © 2018 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Correspondence to: S.L. Shen, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean, and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. ⁎⁎ Corresponding author. E-mail addresses: [email protected] (H.-M. Lyu), [email protected] (S.-L. Shen), [email protected] (A. Arulrajah).

https://doi.org/10.1016/j.scitotenv.2018.01.138 0048-9697/© 2018 Elsevier B.V. All rights reserved.

With the urbanization of mega-cities, a large number of metro lines were constructed to reduce congestion (Shen et al., 2014; Wu et al., 2017a). Their rapid construction caused significant soil disturbance and long-term land subsidence (Galloway and Burbey, 2011; Du et al., 2014; Wu et al., 2017b), as well as increasing impermeable surfaces

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

(Du et al., 2015), which renders cities vulnerable to severe flooding and other extreme weather conditions (Lyu et al., 2016, 2017a; Tan et al., 2016, 2017). Metro system is a vulnerable component of the mass transportation infrastructure system in mega-cities, particularly when subjected to flooding events. Flood disasters have swept billions of dollars of property damage and deaths along with high waters, causing inundation of underground infrastructures (e.g., metro tunnels and facilities). For instance, the flood event with an average hourly rainfall of 60 mm/h in Fukuoka City in June 1999, caused multiple inundations of metro lines, leading to a fatal accident (Herath and Dutta, 2004). Recently, China has suffered from catastrophic flood disasters, particularly in coastal cities (e.g., Guangzhou, Shenzhen, Yancheng, and Wuhan) (Lyu et al., 2016, 2017a). On May 10, 2016, a flash flood event occurred in the Guangzhou metro tunnels and resulted in the inundation of several metro stations. Fig. 1 shows the rainwater inflow into the Changpan Station of metro line 6 in Guangzhou on May 10, 2016 (Lyu et al., 2016). Although the authorities were prepared for this storm, the “May 10th event” still caused catastrophic damage to metro equipment, resulting in a direct economic loss of RMB 543.8 million (≈$82,000,000) in Guangzhou (Lyu et al., 2016). The increasing damage due to flood events has forced authorities in flood-prone cities to reevaluate their policy regarding the resilience of metro systems to future heavy rainfall events. Therefore, it is essential to evaluate the flood risk in metro systems in mega-cities so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. There is an urgent need to develop more accurate models that allow reliable flood assessment in metro systems. This study explores the integration of the analytic hierarchy process (AHP) and the interval AHP (I-AHP) with spatial modeling in Geographic Information System (GIS) for flood risk assessment in metro systems. A multiple index system is developed and then applied to the metro systems in Guangzhou. The flood risk assessments obtained by AHP and I-AHP are compared to provide decision-makers with more informative suggestions for future flooding disasters. 2. Literature review Proper evaluation of flood risk in metro tunnels is challenging. The assessment requires a comprehensive consideration of various factors,

Fig. 1. Changpan Station of metro line 6 flooded during heavy rainfall on May 10th, 2016 in Guangzhou (Lyu et al., 2016).

1013

including watershed features, storm characteristics, and regional characteristics. Owing to the scarcity of data and the complexity as well as the variety of the probability distributions of the factors, there is a great deal of uncertainty in the prediction of flood hazards. In general, there are three types of approaches to evaluate regional flood risk: i) scenario-based simulation analysis (e.g., Amendola et al., 2000; Willems, 2013), ii) multi-index evaluation systems (e.g., Kazakis et al., 2015; Xiao et al., 2017), and iii) probability assessment based on historical data (e.g., Jalayer et al., 2014; Nott, 2006). These approaches are implemented by applying several mathematical theories such as Bayesian network rules (e.g., Pagano et al., 2014; Liu et al., 2016) and AHP (Saaty, 1977; Jalayer et al., 2014). A Bayesian network is initially constructed according to domain knowledge and then tuned by learning from historical data (Li et al., 2010). A consistent system is used for merging multiple sources of data and knowledge to support decision-making. An obvious drawback is that prior probabilities, which are often subjective and inaccurate owing to the complexity and uncertainty of decision-making, should be determined. By contrast, AHP does not require prior probability estimations. It is a comprehensive method, based on multi-critical indexes, for performing both qualitative and quantitative analysis (Saaty, 2008, 2010). In AHP, a scale from 1 to 9 is defined (or their reciprocals) with assigned linguistic terms to represent the relative importance of one object over another (Saaty, 1977). With the judgment matrix, a final decision can be made on the basis of the synthesis results and sensitivity analysis (Jalayer et al., 2014; Liu et al., 2016). The original AHP method uses a single weight to express the relative importance in pairwise comparison analysis. However, it cannot consider the variation of the index as the situation evolves. To overcome this limitation, extensions of AHP have been proposed (Laarhoven and Pedrycz, 1983; Cheng and Mon, 1994; Jin et al., 2016a, 2016b, 2017). The decision process for I-AHP is the same as for the original AHP. The only difference is that I-AHP uses an interval number to express the relative importance of the various factors involved, thus allowing its variation. Entani and Sugihara (2012) proposed an I-AHP method for obtaining the attribute intervals from a given uncertain pairwise comparison matrix. Based on that, Krejci (2015, 2016) applied the extended AHP method with interval fuzzy numbers to establish interval judgment matrices for decision-making. All these studies demonstrated that the AHP and I-AHP methods can be successfully implemented for risk assessment (Deshmukh et al., 2011; Chen et al., 2011; Stefanidis and Stathis, 2013; Li et al., 2013; Yin et al., 2017). However, there are still few studies on flood risk assessment in metro systems. Hashimoto et al. (2003) applied mathematical theories to analyze the flood event in Fukuoka City on June 29, 1999, which caused inundation of the subway and underground space. Subsequently, Aoki et al. (2016) proposed anti-inundation measures for underground stations of Tokyo Metro. Herath and Dutta (2004) proposed a three-dimensional computational model to simulate urban flood events in order to provide informative suggestions on designing more interconnected underground space to reduce flooding risk. Suarez et al. (2005) developed a general framework to estimate the effects of flooding events on the performance of urban transportation networks. Their results showed that the number of travel interruptions would be doubled due to future floods in the following 100 years. Therefore, more detailed research is required for developing accurate assessment of flood risk and countermeasures to enhance disaster resilience to future floods. The objectives of this study are to: i) evaluate the regional flood risk level using both AHP and I-AHP; ii) evaluate the flood risk level in metro systems based on the risk within a range of 500 m along metro lines; and iii) select a reasonable method for flood risk assessment by comparing the results obtained from AHP and I-AHP. The Guangzhou metro

1014

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

system is selected to analyze flood risk, considering the topography, land use, and metro lines. The information presented in this study is expected to benefit both academics and practitioners in the fields of flood disaster planning and metro management.

3.1. Assessment model To evaluate the flood risk of a metro system, the regional flood distribution is first assessed. This is implemented in the following steps: (1) Developing a multiple index system for flood risk assessment that covers the three categories of assessment factors: hazard, exposure, and vulnerability. (2) Determining the weight of each assessment factor using both AHP and I-AHP. (3) Normalizing each factor in hazard, exposure, and vulnerability indexes. (4) Assessing the flood risk distribution in terms of hazard, exposure, and vulnerability indexes. (5) Visualizing the distribution of regional flood risk. (6) Obtaining the regional flood risk within a range of 500 m along metro lines in GIS.

3. Methodology The AHP and I-AHP methods are used for evaluating the flood risk level in metro systems. Fig. 2 shows the procedure of flood risk assessment. It has two parts: one is the structure of the assessment index, whereas the other is a calibration procedure that incorporates the weight of each index into GIS. The assessment system consists of three layers: (1) the object layer, (2) the index layer, and (3) the sub-index layer. Flood risk is the object layer; the index layer includes the hazard, exposure, and vulnerability indexes; the sub-index layer includes various input factors that cover flooding hazard, topology, and metro lines. To obtain a reasonable weight for each factor, both the AHP and I-AHP methods are used to evaluate the relative importance of the various factors. The weight of each assessment factor is then incorporated into GIS to obtain the regional flood risk. The result of flood risk is mapped according to the input spatial data from the flood assessment model. In both the AHP and I-AHP methods, the target is the flood risk of a metro system. It is obviously affected by the regional flood risk within a certain range along metro lines. This range is likely to fluctuate between 500 m and 1000 m. According to field investigations, the range of long-term surface settlement along a metro line is from 500 m to 1000 m (Shen and Xu, 2011; Shen et al., 2014, 2016; Wu et al., 2017a). In this study, the range was set to 500 m, and the flood risk within this region was adopted to represent the flood risk in the metro system.

The risk of a flood disaster is associated with the factors of hazard, exposure, and vulnerability, defined in Eq. (1). Flood risk ¼ Hazard þ Exposure þ Vulnerability

Flood risk is uncertain, resulting from the interactions among hazard, exposure, and vulnerability. Thus, the flood risk assessment model can be expressed by Eq. (2). FR ¼ f ðSk Þ ¼ f ðH; E; VÞ;

Hazard (U1)

E xposure (U2)

R isk level in the range of 500 m around a metro line

V ulnerability (U3) L and use (U31) Metro line proximity (U32)

R ainy season (U11)

E levation (U21) Slope (U22)

A verage rainfall (U12) A verage rainy day (U13)

R iver proximity (U23) R iver density (U24)

Weight calibration (A HP)

Metro line density (U33) R oad network proximity (U34) R oad network density (U35)

Weight calibration (I-A HP)

Normalized assessment index Calibration procedure incorporated into GIS A HP assessment result

ð2Þ

where FR is the flood risk; k the index classification number (when k = 1, it is hazard index; when k = 2, it is exposure index, and when k = 3,

Flood risk for metro system (U)

A ssessment structure

ð1Þ

I-A HP assessment result

Comparison of assessment result

R egional flood risk level

Fig. 2. Procedure of flood risk assessment for metro systems.

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

it is vulnerability index); Sk represents the factors in the assessment model, it can be calculated from Eq. (3): Sk ¼

n X

f k;i F k;i ;

ð3Þ

i¼1

where Fk,i is the normalized value of index i of classification k; fk,I is the P weight of index i of classification k, and ni¼1 f k;i ¼ 1, fk,i N 0. Thus, the assessment model can be redefined by Eq. (4) (Liu et al., 2016):  n  FR ¼ α H ∑ hi H i i¼1



n

Hazard n

!

þ αE ∑ e j E j 

j¼1

þ α V ∑ vk V k k¼1

Exposure

ð4Þ

1015

indexes: land use type (U31), metro line proximity (U32), metro line density (U33), road network proximity (U34), and road network density (U35). Among the vulnerability sub-indexes, more attention is paid to those related to metro lines. The method for constructing the index of proximity and density of metro lines and road networks is the same as that for river networks, which is discussed in Section 4.3.2. Previous studies, which have not considered the metro system, laid more emphasis on the hazard index than on other indexes in flood risk assessment (Sadiq and Tesfamariam, 2009; Liu et al., 2016). According to experts from the offices of metro management and municipal management, the vulnerability index is more significant than the hazard and exposure indexes. In the risk assessment structure for the metro system, the vulnerability index is considered the most important index. Therefore, in this study, great weight is placed on the role of this index.

Vu ln erability

3.3. Weight calibration where αH, αE, and αV are the weights of the hazard index, exposure index, and vulnerability index, respectively; hi, ej, and vk are the same as fh,i in Eq. (3); Hi, Ej, and Vk correspond to Fk,i for k = 1, 2, 3 in Eq. (3). 3.2. Index system The hierarchical assessment structure consists of the object layer, the index layer, and the sub-index layer. The object layer is the flood risk of the metro system, whose index is labeled as U. The index layer includes three categories: hazard, exposure, and vulnerability, whose indexes are labeled as U1, U2, and U3, respectively. The sub-index layer covers a total of 12 sub-indexes that correspond to hazard, exposure, and vulnerability (see Fig. 2). Based on this hierarchical assessment structure, AHP and I-AHP are used for calculating the weights of the assessment indexes. Both the weights calculated by AHP and I-AHP are incorporated into GIS to obtain a regional flood risk level. Moreover, a comparison between the results obtained from AHP and I-AHP is made to verify the suitability of I-AHP. The risk level within a range of 500 m along a metro line is obtained to evaluate the flood risk of the metro system. (1) Hazard index

The hazard index is used for representing the characteristics of flood hazard, which in turn is represented by three sub-indexes: rainfall in the rainy season (U11), average annual rainfall (U12), and annual rainstorm days (U13). Research on the regional precipitation in China has shown that the Kriging interpolation method can accurately reproduce the spatial distribution pattern of rainfall events (Cattle et al., 2002; Bargaoui and Chebbi, 2009), and it is performed by interpolating average annual rainfall and annual rainstorm days for the entire region. (2) Exposure index

The exposure index reflects the properties of the disaster bearing body. The exposure index is represented by topographic elevation (U21 ), slope (U22 ), river proximity (U23 ), and river density (U 24 ). Data for elevation and slope are obtained from the digital elevation model (DEM) by resampling in GIS. The process for acquiring data of river network proximity and density is further discussed in Section 4.3.2. (3) Vulnerability index

The vulnerability index reflects the resistance of the disaster bearing body. The vulnerability index is represented by the following sub-

3.3.1. AHP method The judgment matrix in the flood risk assessment model reflects the relative importance of each factor. It is presented in Eq. (5). In the judgment matrix, two factors ai and aj are chosen at each time step. When ai is significantly more important than aj, aij is set to 9, and aji is set to 1/9. 0 1   Au ¼ aij nn ¼ @ ⋮ an1

1 … a1n ⋱ ⋮ A ⋯ 1

ð5Þ

where Au is the judgment matrix, aij the relative importance of factor i to factor j, which ranges from 1 to 9, and its reciprocal, according to Saaty's original proposal (Saaty, 1977). Then, the weights (w) of the factors can be calculated from Eq. (6): M wi ¼ Pn i i¼1

ð6Þ

Mi

where M i ¼

ffi qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Qn n j¼1 aij .

The value of the consistency ratio (CR), which can be calculated by Eq. (7), is used to evaluate the sensitivity and consistency of the judgment matrix. According to Saaty (1977), if CR is N0.1, then the judgment matrix is unreasonable and must be re-determined.

CR ¼

CI RI

ð7Þ

where CI = (λmax − n)/(n − 1) and λmax is the largest eigenvalue of the judgment matrix, which can be calculated from Eq. (8). RI is the average random consistency index which can be referred in Lyu et al. (2017b).

λmax ¼

n X i¼1

Pn j¼1

aij wi

nwi

ð8Þ

3.3.2. I-AHP In decision-making, an exact value cannot adequately express the decision maker's opinion in pairwise comparisons (Wang and Chen, 2007; Nezarat et al., 2015). In this case, I-AHP uses an interval number, rather than a crisp number, to assess the relative importance. The interval pairwise comparison matrix consists of interval

1016

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

numbers from Eq. (9).The judgment matrix of I-AHP is separated into two matrices: lower bound matrix and upper bound matrix.

    þ A ¼ A− Aþ ¼ aij nn ¼ a i j ; ai j 0   þ   þ  1 þ ⋯ a1i a1i ⋯ a ½1; 1 a12 a12 1n a1n B C B   þ  C B 1 1 þ C B þ  ½11 ⋯ a2i a2i a ⋯ a 2n 2n C B a12 a12 C B C B C B C ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ B C B C  ¼B 1 1   þ C 1 1 B C a ⋯ ½ 11  ⋯ a in in C B aþ a  aþ B 1i 1i C 2i a2i B C B C ⋯ ⋯ ⋯ ⋯ ⋯ ⋯ B C B C   B C @ 1 1 A 1 1 1 1 ⋯ ⋯ ½ 11  þ  þ  þ  a1n a1n a2n a2n ain ain

ð9Þ

4.2. Weight calibration

where A− is the lower bound matrix, A+ is the upper bound matrix, ða and ðaþ Þ are elements in the judgment matrices A− and ij Þ ij nn

nn

A+, respectively, and 1=9≤ða ij Þ

nn

≤ðaþ Þ ij

nn

≤9.

The weights of the interval pairwise comparison matrix can be calculated using the eigenvectors corresponding to the maximum eigenvalue (Entani and Sugihara, 2012). The weight vector of the interval pairwise comparison matrix is calculated from Eq. (10). ! w ¼ ½w1 ; w2  ¼ ½αw− ; βwþ  "

n

#1=2

where α ¼ ∑ ∑n j¼1

1

i¼1

aþ ij

ð10Þ "

n

4.2.1. AHP calculation The judgment matrix of the index layer to the object layer can be obtained according to the aforementioned method. The weight of each assessment factor can be calculated by Eq. (6); the largest eigenvalue (λmax) of the judgment matrix can be calculated from Eq. (8); the CR can be calculated from Eq. (7). The other judgment matrices of the sub-index layer to index layer can also be obtained. The weight of each judgment matrix can be calculated using the aforementioned method as well. The detailed judgment matrix and weight calculation process can be found in the companion data (Lyu et al., 2017b).

#1=2

, β ¼ ∑ ∑n j¼1

water inundations at several metro stations in Guangzhou (Lyu et al., 2016). Fig. 3 shows the administrative region of Guangzhou with the metro system. The total area of Guangzhou reaches 7434.4 km2, covering four districts in the urban center and seven districts in suburban areas. As shown in Fig. 3, there are 15 metro lines distributed in Guangzhou, where ten metro lines are in operation and five other metro lines are under construction. In this study, the following data were used to assess the flood risk in Guangzhou: (1) rainfall data obtained from the website of Guangzhou Weather (http://www.tqyb.com.cn/) and publications (Huang et al., 2017; Lin, 2007); (2) DEM topographic data from the Geospatial Data Cloud (http://www.gscloud.cn/); (3) data related to rivers and other water bodies from the National Geomatics Centre of China (http://ngcc.sbsm.gov.cn/); (4) land use types from remote sensing image data.

1

i¼1

a ij

, w− and w+ are the

weights of the lower bound and upper bound matrices, respectively, and w1 and w2 are the weights of the interval pairwise comparison matrix. The sensitivity and consistency of the interval pairwise comparison matrix is determined as Eq. (11). w1 ≤w ≤w2 ;

ð11Þ

where w is the weight calculated by AHP. 3.4. Normalization To facilitate the comparison between various indexes, the value of each sub-index is normalized over the range from zero to one. In the assessment index system, flood risk will decrease with the increase of the following five factors: topographic elevation and slope, areal density of rivers and metro lines, and road network. These five factors are negative sub-indexes. The other factors are positive sub-indexes. The positive indexes are normalized using Eq. (12), whereas the negative indexes are normalized using Eq. (13). iij ¼

iij0 −ip min ip max −ip min

ð12Þ

iij ¼

ip max −iij0 ; ip max −ip min

ð13Þ

where iij is the normalized value of the sub-index, iij0 is the original value of the factor, and ipmax and ipmin are the maximum and minimum values of the sub-index, respectively. 4. Case study 4.1. Background Guangzhou is a coastal city, located at latitude 22°50′ and longitude between 112°50′ and 114°20′E. Recently, severe floods resulted in flood

4.2.2. I-AHP calculation The interval pairwise comparison matrix of the index layer to the object layer can be established by Eq. (9), and similarly the sub-index layer to the index layer can be obtained by Eq. (9) (Lyu et al., 2017b). In an interval matrix, the value of w− and w+ are calculated based on the matrix of A− and A+, as shown in Eq. (10), which can be calculated as in the AHP method. The values of α and β are calculated using A− and ! A+. After the calculation of w−, w+, α, and β, the weight vector (w ) of the interval pairwise comparison matrix can be obtained by Eq. (10). The weight of each interval pairwise comparison matrix can be calculat! ed using the method in Section 3.3.1. The normalized weight vector (w 0 ) can then be calculated by Eqs. (12) and (13), which is incorporated into GIS to evaluate the regional flood risk level. The weights calculated by AHP and I-AHP are listed in Table 1. It can be seen that before normalization, the AHP weights fall into the range of the I-AHP weights. However, after normalization, this may no longer hold true. 4.3. Analysis 4.3.1. Analysis of hazard index Based on published data, the flooding season in Guangzhou lasts from May to September. Rainfall data from 1951 to 2015 were collected (Lin, 2007; Huang et al., 2017), when the hazard index was obtained using the raster calculation tool in GIS. Fig. 4 shows the distribution of average annual rainfall (Fig. 4a), annual rainstorm days (Fig. 4b), and hazard index level (Fig. 4c). As shown in Fig. 4, the rainfall in the north-eastern part exceeds that in south-western Guangzhou. The urban center had relatively less rainfall from 1951 to 2015. These data were visualized using Kriging interpolation in GIS based on the distribution of meteorological stations shown in Fig. 5a. 4.3.2. Analysis of exposure index The exposure index represents topographical features and drainage systems. Fig. 5 shows the topographical characteristics of the study area. Fig. 5a shows the characteristics of the local river system and the locations of meteorological stations. In the administrative region of Guangzhou, the river system consists of streams, rivers, and water bodies. The

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

1017

Fig. 3. Administrative region of Guangzhou with the metro system.

spatial distributions of elevation and slope are shown in Fig. 5b and c, respectively. River proximity and river density reflect the characteristics of this drainage system (Liu et al., 2016). Fig. 5d and e shows the classification of river proximity and density, respectively. River proximity refers to the distance to the closest river channels, whereas river density indicates the length of river channel per unit area. In the determination of river proximity and density, the critical levels of proximity were set to 200 m, 400 m, 600 m, 800 m, and 1000 m; density was computed within a circle of radius 1.6 km. After the normalization of each sub-index, based on the weights of each exposure index listed in Table 1, the raster calculation in GIS was employed to process different factors. Fig. 5f shows the result for the exposure index in an overlay analysis. As shown in Fig. 5f, the area with a high level of vulnerability is located in the south, where there are complex river systems at a low elevation with a flat slope. 4.3.3. Analysis of vulnerability index The vulnerability index includes the sub-indexes of land use, metro system, and major road network. Different land use results in different

levels of resistance to flooding. Fig. 6a shows the different types of land use, and Fig. 6b shows the classification of land use. Residential areas and factories have high population densities. Flood disasters always result in catastrophic losses in these areas. Therefore, the vulnerability of residential areas and factories is assessed as being at a very high level. By contrast, forest and unused land are less vulnerable to flooding, as rainstorms seldom cause flood disasters in forest land or unused land (Yao et al., 2009, Yao and Zhou, 2013). A water body that can discharge rainwater and induce inundation simultaneously is assessed as a medium-level vulnerability factor. Based on this analysis, land use is categorized into five levels from very low to very high. As shown in Fig. 6b, the vulnerability of the urban center is very high. Northern Guangzhou is mainly covered by forests; thus, its flood risk is very low. To consider the influence of urbanization, the presence of a metro line is regarded as a vulnerability factor. This study uses the same method that was used in the analysis of the river system to assess the vulnerability of metro lines to flood hazards. The proximity of a metro line is defined as the distance to the closest metro line. The critical levels of

Table 1 AHP weights and I-AHP weights. Index layer

Sub-index layer

Ui

AHP

! I-AHP (w )

! Normalized (w 0 )

Uij

AHP

! I-AHP (w )

! Normalized (w 0 )

U1

0.2493

[0.2400,0.2687]

[0.2658,0.2482]

U2

0.1571

[0.1526,0.1709]

[0.1690,0.1578]

U3

0.5936

[0.5101,0.6432]

[0.5651,0.5940]

U11 U12 U13 U21 U22 U23 U24 U31 U32 U33 U34 U35

0.1191 0.5816 0.3093 0.3037 0.4617 0.1023 0.1332 0.3626 0.1273 0.2188 0.0775 0.2137

[0.1176,0.1196] [0.5271,0.5980] [0.2964,0.3573] [0.2668,0.3328] [0.4108,0.4897] [0.0943,0.1177] [0.1241,0.1480] [0.3246,0.3749] [0.1140,0.1334] [0.1956,0.2309] [0.0745,0.0839] [0.1889,0.2391]

[0.1249,0.1112] [0.5601,0.5563] [0.3149,0.3325] [0.2978,0.3058] [0.4584,0.4500] [0.1052,0.1082] [0.1385,0.1360] [0.3616,0.3529] [0.1270,0.1256] [0.2179,0.2174] [0.0829,0.0789] [0.2105,0.2251]

1018

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

Fig. 4. Spatial distribution of hazard indexes from 1951 to 2015: (a) average annual rainfall; (b) annual rainstorm days; (c) distribution of hazard index level.

proximity were set to 200 m, 400 m, 600 m, 800 m, and 1000 m. The density of a metro line is defined as the length of metro line per square kilometer and is obtained using a circle of radius 1000 m. Fig. 7a and b shows the distributions of metro line proximity and density, respectively. It can be seen that metro lines are more densely distributed in the urban center. A flood may lead to transportation disruption on the road network. To assess the influence of flooding, the road network is considered a vulnerability index in the assessment of flood risk. Fig. 8a shows the distribution of major roads. In the analytical procedure, using the same method, the critical levels of the proximity of a road network are set to 200 m, 400 m, 600 m, 800 m, and 1000 m (Fig. 8b). The density of a road network is calculated using a circle of radius 800 m (Fig. 8c). To obtain the vulnerability level, the sub-indexes of land use level, metro line proximity, metro line density, road network proximity, and road network density are normalized. Based on the weight of each sub-index in Table 1, the vulnerability level is obtained using the raster calculation tool in GIS, which combines assessment indexes with their weights. Fig. 8d shows the vulnerability level.

4.4. Flood risk mapping and evaluation According to the normalized weights listed in Table 1 and the risk level of each index, the spatial distribution of flood risk was classified into five levels (from very low to very high) using reclassification tools in GIS. Fig. 9 shows the flood risk distribution obtained by AHP (Fig. 9a) and I-AHP (Fig. 9b: lower bound; Fig. 9c: upper bound). It should be noted that the AHP results are not within the range of upper bound and lower bound of the I-AHP results because the normalized weights (see Table 1) were applied to the overlay analysis. Results obtained by normalized weights reflect the comparative difference between AHP and I-AHP. Moreover, this phenomenon demonstrates that I-AHP yields a wider assessment than AHP. The comparative results show that the lower bound of I-AHP is similar to the upper bound of I-AHP, as the lower and upper bounds of the normalized weights are very close in overlay analysis in GIS. After evaluating the regional flood risk, the risk level along the metro line can be obtained using GIS tools; thus, the flood risk of the metro system can be assessed. Fig. 10 shows the flood risk level along metro lines, with the Changpan station of metro line 6 highlighted. The flood risk

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

Fig. 5. Topography in study area (a) river system; (b) elevation; (c) slope; (d) river proximity; (e) river density; (f) distribution of exposure index level.

1019

1020

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

Fig. 6. Land use mapping: (a) type; (b) classification.

obtained by AHP and I-AHP is shown in Fig. 10a and b, respectively. The upper bound is shown in Fig. 10c. The AHP results show Changpan Station with a high risk level, whereas the upper bound of the I-AHP result shows Changpan Station with a very high risk level. In the “May 10th event,” Changpan Station of metro line 6 flooded in rainwater, with a

water depth of 0.5–0.8 m (Lyu et al., 2016). The AHP result shows Changpan Station with a high level risk, whereas the upper bound of the I-AHP result shows Changpan Station with a very high risk level. This comparison demonstrates that I-AHP yields a more reasonable assessment of flood risk.

Fig. 7. Influence of metro line: (a) metro line proximity; (b) metro line density.

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

1021

Fig. 8. Road network and vulnerability level: (a) main road network; (b) road network proximity; (c) road network density; (d) spatial distribution of vulnerability level.

4.5. Validation The “May 10th event” affected more than one million individuals and resulted in eight fatalities. Owing to the limited damage data for this event, the casualty distribution was used to validate the assessment of flood risk. Fig. 9 shows the fatality locations for the “May 10th event.” It is assumed that a fatality location indicates very high risk level. As shown in Fig. 9, all fatality locations fall into the very high risk level region in the I-AHP results in Fig. 9b and c; however, they are not perfectly consistent with the high risk region in the AHP results in Fig. 9a. Fig. 10 shows the location of metro line 6 and Changpan Station. The assessment result shows the metro line 6 with a very high risk level. The disaster of the “May 10th event” is consistent with the I-AHP result. This

implies that the I-AHP method yields more accurate predictions of flood risk than AHP. 5. Discussion 5.1. Comparison of the AHP and I-AHP results The flood risk of the metro system is related to the flood risk of the surrounding region along metro lines. In particular, the potential flood risk is affected by the flood risk within a range of 500 m along the metro line. Both AHP and I-AHP were used to evaluate the regional spatial distribution of flood risk. Fig. 11 shows the comparative results of flood risk assessed both from AHP and I-AHP. As

1022

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

Fig. 9. Spatial distribution of regional flood risk: (a) AHP result; (b) lower bound of I-AHP result; (c) upper bound of I-AHP result.

shown in Fig. 11, for the regional risk, the ratios of high risk level and very high risk level evaluated by I-AHP are larger than those by AHP. For the risk level of metro lines, the ratio of very high risk level evaluated by I-AHP is larger than that by AHP. This difference indicates that I-AHP can identify the greatest flood risk for a metro system. Consequently, more attention should be paid to the vulnerable sections of the metro system in future severe flooding events. Thus, the I-AHP model of flood risk assessment can provide more informative suggestions regarding both precaution and post-event emergency evacuation. 5.2. Implications In previous research on flood risk assessment, the hazard index was the most significant among the hazard, exposure, and vulnerability

indexes (Sadiq and Tesfamariam, 2009; Liu et al., 2016). However, the vulnerability index appears to contribute more to the flood risk level than the hazard and exposure indexes because the metro lines are the transportation infrastructures most vulnerable to flooding in mega-cities. Experts in metro management also suggest considering the vulnerability of metro lines in the assuagement of flood risks in mega-cities. Therefore, the vulnerability index has the largest weight in flooding risk assessment. The assessment results show that the existence of metro lines can increase the vulnerability of mega-cities to flooding. The flood risk level at regional scale may aggravate, leading to cascading failures across interdependent infrastructure systems. Moreover, the densest distributions of metro lines are located in high and very high flood risk areas. It is recommended that, during the process of metro line construction, countermeasures for resisting future floodwater should be developed (such as increasing the step height of exiting

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

Fig. 10. Flood risk along metro lines of Guangzhou: (a) AHP result; (b) lower bound of I-AHP result; (c) upper bound of I-AHP result.

Fig. 11. Ratio of different risk levels of AHP and I-AHP: (a) risk level on regional level; (b) risk level along metro line.

1023

1024

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025

stations) and the safety of subway stations should be improved using more powerful drainage systems. To protect metro lines from flooding, further research should be conducted on flood risk assessment along metro lines, particularly at exit stations. 6. Conclusions The AHP and I-AHP methods were used for assessing regional flood risk, particularly focusing on the flood risk of metro systems. A case study of the Guangzhou metro system was presented to compare the results obtained by AHP and I-AHP. The conclusions are summarized as follows. 1. The AHP and I-AHP methods were used for assessing regional flood risk and flood risks of the metro system in Guangzhou. The results show that metro lines are densely distributed in urban central areas of high and very high flood risk. 2. The comparison between the AHP and I-AHP results demonstrates that I-AHP yields a more reasonable assessment than AHP. I-AHP uses an interval number to capture the evolution effect, thus overcoming the drawback of the AHP method. With the interval weights incorporated into GIS, I-AHP can obtain a reasonable assessment of flood risk. 3. The results from both AHP and I-AHP indicate that the Guangzhou metro system is vulnerable to floods. The method in this study can be easily applied to other cities and their metro systems. Assessment results can support more informative decision-making in developing disaster plans for metro systems. Acknowledgments The work described was funded by the National Natural Science Foundation of China (NSFC) (Grant No. 41672259). This financial support is gratefully acknowledged. References Amendola, A., Ermoliev, Y., Ermolieva, T.Y., Gitis, V., Koff, G., Linnerooth-Bayer, J., 2000. A systems approach to modeling catastrophic risk and insurability. Nat. Hazards 21 (2– 3), 381–393. Aoki, Y., Yoshizawa, A., Taminato, T., 2016. Anti-inundation measures for underground stations of Tokyo Metro. Procedia Eng. 165, 2–10. Bargaoui, Z.K., Chebbi, A., 2009. Comparison of two kriging interpolation methods applied to spatiotemporal rainfall. J. Hydrol. 365 (1), 56–73. Cattle, J.A., McBratney, A., Minasny, B., 2002. Kriging method evaluation for assessing the spatial distribution of urban soil lead contamination. J. Environ. Qual. 31 (5), 1576–1588. Chen, Y., Yeh, C., Yu, B., 2011. Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Nat. Hazards 59, 1261–1276. Cheng, C.H., Mon, D.L., 1994. Evaluating weapon system by analytical hierarchy process based on fuzzy scales. Fuzzy Sets Syst. 63, 1–10. Deshmukh, A., Oh, E.H., Hastak, M., 2011. Impact of flood damaged critical infrastructure on communities and industries. Built Environment Project and Asset Management. Vol. 1, pp. 156–175. Du, S., Shi, P., Van Rompaey, A., Wen, J., 2015. Quantifying the impact of impervious surface location on flood peak discharge in urban areas. Nat. Hazards 76, 1457–1471. Du, Y.J., Wei, M.L., Reddy, K.R., Liu, Z.P., Jin, F., 2014. Effect of acid rain pH on leaching behavior of cement stabilized lead-contaminated soil. J. Hazard. Mater 271, 131–140. Entani, T., Sugihara, K., 2012. Uncertainty index based interval assignment by interval AHP. Eur. J. Oper. Res. 219 (2), 379–385. Galloway, D.L., Burbey, T.J., 2011. Review: regional land subsidence accompanying groundwater extraction. Hydrogeol. J. 19 (8), 1459–1486. Hashimoto, H., Park, K., Watanabe, M., 2003. Overland flood flow around the JR Hakataeki station from the Mikasa and Sanno-Channel River in Fukuoka City on June 29, 1999. J. Japan Soc. Nat. Dis. Sci. 21 (4), 369–384. Herath, S., Dutta, D., 2004. Modeling of urban flooding including underground space. Proceedings of the Second International Conference of Asia-Pacific Hydrology and Water Resources Association. 2004, pp. 55–63. Huang, J.L., Fan, J.W., Tang, Z.Z., 2017. Correlation Study of Rainstorm and Tidal Level Using the Maximum Entropy-Copula Method: A Case Study of Guangzhou. pp. 65–71 (in Chinese). Jalayer, F., De Risi, R., Giugni, M., Manfredi, G., Gasparini, P., Cavan, G., 2014. Probabilistic GIS-based method for delineation of urban flooding risk hotspots. Nat. Hazards 73 (2), 975–1001.

Jin, Y.F., Yin, Z.Y., Shen, S.L., Hicher, P.Y., 2016a. Selection of sand models and identification of parameters using an enhanced genetic algorithm. Int. J. Numer. Anal. Methods Geomech. 40 (8), 1219–1240. Jin, Y.F., Yin, Z.Y., Shen, S.L., Hicher, P.Y., 2016b. Investigation into MOGA for identifying parameters of a critical-state-based sand model and parameters correlation by factor analysis. Acta Geotech. 11 (5), 1131–1145. Jin, Y.F., Wu, Z.X., Yin, Z.Y., Shen, J.S., 2017. Estimation of critical state-related formula in advanced constitutive modeling of granular material. Acta Geotech. 12 (6), 1329–1351. Kazakis, N., Kougias, I., Patsialis, T., 2015. Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: application in Rhodope–Evros region, Greece. Sci. Total Environ. 538, 555–563. Krejci, J., 2015. Additively reciprocal fuzzy pairwise comparison matrices and multiplicative fuzzy priorities. Soft. Comput. 21 (12), 3177–3192. Krejci, J., 2016. Obtaining fuzzy priorities from additive fuzzy pairwise comparison matrices. IMA J. Manag. Math.:dpw006 https://doi.org/10.1093/imaman/dpw006 (Published online). Laarhoven, P.J.M.V., Pedrycz, W., 1983. A fuzzy extension of saaty's priority theory. Fuzzy Sets Syst. 11 (1–3), 199–227. Li, L., Wang, J., Leung, H., Jiang, C., 2010. Assessment of catastrophic risk using Bayesian network constructed from domain knowledge and spatial Data. Risk Anal. 30 (7), 1157–1175. Li, F., Phoon, K.K., Du, X., Zhang, M., 2013. Improved AHP method and its application in risk identification. J. Constr. Eng. Manag. 139 (3), 312–320. Lin, P.S., 2007. Analysis on the climate characteristic and climatic change in Guangzhou City in last 50 years. J. Liaocheng Univ. (Nat. Sci.). 20 (3), 81–84 (in Chinese). Liu, R., Chen, Y., Wu, J., Gao, L., Barrett, D., Xu, T., Yu, J., 2016. Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia. Stoch. Env. Res. Risk A. 30 (6), 1575–1590. Lyu, H.M., Wang, G.F., Shen, J.S., Lu, L.H., Wang, G.Q., 2016. Analysis and GIS mapping of flooding hazards on 10 May, 2016, Guangzhou, China. Water 8 (447):1–17. https:// doi.org/10.3390/w8100447. Lyu, H.M., Wang, G.F., Cheng, W.C., Shen, S.L., 2017a. Tornado hazards on June 23rd in Jiangsu Province, China: preliminary investigation and analysis. Nat. Hazards 85 (1), 597–604. Lyu, H.M., Shen, S.L., Sun, W.J., Arulrajah, A., 2017b. Data on Flood Risk Assessment of Metro System in Mega-cities Using AHP and I-AHP Incorporated into GIS: Comparative Study Data in Brief, under review. Nezarat, H., Sereshki, F., Ataei, M., 2015. Ranking of geological risks in mechanized tunneling by using Fuzzy Analytical Hierarchy Process (FAHP). Tunn. Undergr. Space Technol. 50, 358–364. Nott, J., 2006. Extreme Events: A Physical Reconstruction and Risk Assessment. Cambridge University Press. Pagano, A., Giordano, R., Portoghese, I., Fratino, U., Vurro, M., 2014. A Bayesian vulnerability assessment tool for drinking water mains under extreme events. Nat. Hazards 74 (3), 2193–2227. Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281. Saaty, T.L., 2008. Decision making with the analytic hierarchy process. Int. J. Services Sci. 1 (1), 83–98. Saaty, T.L., 2010. Creative Thinking, Problem Solving and Decision Making (Third Edition with Chinese Translation) (Rws Pubns). Sadiq, R., Tesfamariam, S., 2009. Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP). Stoch. Env. Res. Risk A. 23 (1), 75–91. Shen, S.L., Xu, Y.S., 2011. Numerical evaluation of land subsidence induced by groundwater pumping in Shanghai. Can. Geotech. J. 48 (9), 1378–1392. Shen, S.L., Wu, H.N., Cui, Y.J., Yin, Z.Y., 2014. Long-term settlement behavior of the metro tunnel in Shanghai. Tunn. Undergr. Space Technol. 40, 309–323. Shen, S.L., Cui, Q.L., Ho, E.C., Xu, Y.S., 2016. Ground response to multiple parallel microtunneling operations in cemented silty clay and sand. J. Geotech. Geoenviron. 142 (5):1–11. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001441 (04016001). Stefanidis, S., Stathis, D., 2013. Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Nat. Hazards 67, 569–585. Suarez, P., Anderson, W., Mahal, V., Lakshmanan, T.R., 2005. Impacts of flooding and climate change on urban transportation: a systemwide performance assessment of the Boston Metro Area. Transp. Res. Part D: Transp. Environ. 10 (3), 231–244. Tan, Y., Huang, R., Kang, Z., Wei, B., 2016. Covered semi-top-down excavation of subway station surrounded by closely spaced buildings in downtown Shanghai: building response. J. Perform. Constr. Facil. 30 (6), 04016040. Tan, Y., Zhu, H., Peng, F., Karlsrud, K., Wei, B., 2017. Characterization of semi-top-down excavation for subway station in Shanghai soft ground. Tunn. Undergr. Space Technol. 68 (2017), 244–261. Wang, T.C., Chen, Y.H., 2007. Applying consistent fuzzy preference relations to partnership selection. Omega 35 (4), 384–388. Willems, P., 2013. Revision of urban drainage design rules after assessment of climate change impacts on precipitation extremes at Uccle, Belgium. J. Hydrol. 496, 166–177. Wu, H.N., Shen, S.L., Yang, J., 2017a. Identification of tunnel settlement caused by land subsidence in soft deposit of Shanghai. J. Perform. Constr. Facil. 31 (6), 04017092. Wu, Y.X., Shen, J.S., Cheng, W.C., Hino, T., 2017b. Semi-analytical solution to pumping test data with barrier, wellbore storage, and partial penetration effects. Eng. Geol. 226, 44–51. Xiao, Y., Yi, S., Tang, Z., 2017. Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Sci. Total Environ. 599, 1034–1046.

H.-M. Lyu et al. / Science of the Total Environment 626 (2018) 1012–1025 Yao, Y.P., Zhou, A.N., 2013. Non-isothermal unified hardening model: a thermo-elastoplastic model for clays. Geotechnique 63 (15), 1328. Yao, Y.P., Hou, W., Zhou, A.N., 2009. UH model: three-dimensional unified hardening model for overconsolidated clays. Geotechnique 59 (5), 451–469.

1025

Yin, Z.Y., Jin, Y.F., Shen, J.S., Hicher, P.Y., 2018. Optimization techniques for identifying soil parameters in geotechnical engineering: comparative study and enhancement. Int. J. Numer. Anal. Methods Geomech. 42:70–94. https://doi.org/10.1002/nag.2714.