UsingMultipleIndexComprehensiveMethodtoAssessUrban...

Research ArticleUsing Multiple Index Comprehensive Method to Assess UrbanRainstorm Disaster Risk in Jiangsu Province China

Junfei Chen 123 Mengchen Chen1 and Pei Zhou1

1Business School Hohai University Nanjing 210098 China2Yangtze River Conservation and Green Development Institute Hohai University Nanjing 210098 China3Jiangsu Yangtze River Conservation and High-quality Development Research Center Hohai University Nanjing 210098 China

Correspondence should be addressed to Junfei Chen chenjunfeihhueducn

Received 21 December 2019 Revised 18 March 2020 Accepted 18 April 2020 Published 21 July 2020

Academic Editor Luis Cea

Copyright copy 2020 Junfei Chen et al (is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

An integrated index system for urban rainstorm risk evaluation has been developed Meanwhile an information diffusion method(IDM) and variable fuzzy sets (VFSs) were employed to evaluate the dangerousness sensitivity and vulnerability risk of urbanrainstorm disasters respectively(en the comprehensive risk zoningmap was drawn Finally Jiangsu Province has been taken asa case study area Due to heavy rainfall in short-term and low rainstorm resistance abilityWuxi Changzhou Nanjing and Suzhouhave higher dangerousness whileWuxi Changzhou and Nanjing have higher sensitivity And because of potential losses in urbanrainstorm disaster Wuxi and Suzhou have higher vulnerability than other cities(e comprehensive risk zoning map showed thatmost cities of Jiangsu Province are at the moderate risk level and the northwestern cities have lower risk level than the southerncities (e results are consistent with the actual situation of Jiangsu Province and the study can provide some decision-makingreferences for the urban rainstorm management

1 Introduction

With the global climate change and the rapid developmentsof the urbanization many cities are suffered extremerainstorm events frequently [1ndash4] According to EM-DATand Munich RE disaster databases about 15 of theworldwide population is under the threat of the rainstormdisasters and flood losses account for 28 of total globaldisaster losses (e more frequent urban rainstorm disastersin big cities have brought about severe economic losses[5 6] In China more than 600 cities are exposed to frequentfloods which have caused adverse influence on socialeconomic environmental and stability development [7 8](erefore it is vital to evaluate urban rainstorm disaster riskwhich can help to improve the prediction ability and toreduce the losses caused by urban rainstorm disasters

In the last decades several disaster risk assessmentsystems for urban rainstorm have been developed Lyu [9]proposed a risk assessment system based on rainy seasonaverage rainfall river proximity and other rainstorm

indices Alfa [10] developed a flood risk assessment system ofOfu River catchment in Nigeria including elevation slopeproximity and soil type Weerasinghe [11] put forward therisk assessment system of rainstorm for the WesternProvince of Sri Lanka In the system they adopted a sta-tistical expression of hazard exposure and vulnerability toassess the combined flood risk levels Chen [12] used theradial basis function (RBF) neural network to assess theurban flood risk of the Yangtze River Delta China during2009 to 2018 In combination with urban plan and hydro-logic data Wang [13] developed a generalized risk assess-ment model of pipeline network According to the 1302historical sample data Shao [14] adopted the gray fixedweight cluster analysis to assess the disaster losses from 2004to 2009(ese research studies laid a good foundation for therisk assessment index system of urban rainstorm disastersConsidering that urban rainstorm risk assessment involveshazard-formative factors hazard-inducing environmentsand hazard-affected body this paper establishes the indexsystem with dangerousness of hazard-formative factors

HindawiMathematical Problems in EngineeringVolume 2020 Article ID 8973025 10 pageshttpsdoiorg10115520208973025

sensitivity of hazard-inducing environments and vulnera-bility of hazard-affected body

Meanwhile GIS (the Geographic Information System)and remote-sensing imagery [15ndash18] are adopted to assessthe risk assessment of urban rainstorm disasters Howeverdue to lack of sufficient data urban rainstorm disasters aredifficult to assess accurately [19] (erefore fuzzy and un-certainty theories were introduced into the risk assessmentof urban rainstorm disasters Ren [20] adopted variablefuzzy sets to assess the flood risk in Chengdu China Wang[21] evaluated the flood risk in Guizhou based on the in-formation diffusion method Zou [22] adopted informationdiffusion to analyze the flood risk However there are somedrawbacks in the above methods For example the infor-mation diffusionmethod (IDM) cannot assess the subsystemrisk in the rainstorm assessment while the variable fuzzy sets(VFSs) are difficult to determine the classification standardsobjectively [23] Chen [24] showed that the IDM can help theVFS to establish the classification standards and the VFS canobtain the subsystems and comprehensive risk evaluationresults (erefore in this paper a brand-new domain ldquotherisk of urban rainstorm disastersrdquo is studied We build a newevaluation index system in this paper Urban rainstorm is afuzzy phenomenon and lack of sufficient data traditionalstatistics are difficult to depict it accurately especially insmall sample problems (erefore some fuzzy and uncer-tainty theories have been developed to assess the risk ofurban rainstorm disasters(e variable fuzzy sets (VFSs) canmake full use of various index data to obtain comprehensiverisk evaluation results (e information diffusion method(IDM) can transform a sample observed value into a fuzzyset and is capable of dealing with small sample problemsMeanwhile IDM can extract useful information and es-tablish the level classification standards of urban rainstormdisasters risk assessment indices which is helpful to de-termine the relative membership function of the VFS(erefore in this research the level classification standardsof each risk assessment index of urban rainstorm disasterswere calculated by the IDM and then risks of dangerous-ness sensitivity and vulnerability were obtained by the VFSFinally Jiangsu Province in China was taken as a case studyarea

2 Overview of Study Area

(is paper takes Jiangsu Province as the study area JiangsuProvince (Figure 1) is located between 116deg18primeEndash121deg57primeEand 30deg45primeNndash35deg20primeN (e total area is 1072times105 km2accounting for 11 area of China (e geomorphology ofJiangsu is mainly plains and the elevation of most area islower than 50m [25] (ere are 13 prefecture-level cities inJiangsu Province including Nanjing (provincial capital city)Wuxi Xuzhou Changzhou Suzhou Nantong Lia-nyungang Huairsquoan Yancheng Yangzhou ZhenjiangTaizhou and Suqian Jiangsu Province belongs to the sub-tropical monsoon climate and thus most of the large-scaleprecipitation is happed in summer To better show the actualsituations of the urban rainstorm disasters of Jiangsu

Province this paper emphasizes research the period fromJune to August

Jiangsu Province is one of the most urbanized regions inChina In recent years urban rainstorm disasters have oc-curred more frequently and caused huge losses in JiangsuProvince(us urban rainstorm has become one of the mostimportant factors that restrict the development of JiangsuProvince

3 Data and Methods

31 Data Sources (is paper uses Jiangsu Statistical Year-book data and China City Statistical Yearbook data to collectthe statistical information of all cities in Jiangsu Provinceincluding economic social demographic urban construc-tion environmental and other related city statistics (emeteorological data and the rainfall statistics are collectedfrom the meteorological stations of cities in Jiangsu Prov-ince Other historical precipitation statistics are provided bythe Jiangsu climate center Among these data the contin-uous rainfall days and heavy rain days are collected bymonthly from 2010 to 2016 from the Jiangsu climate center

32 Weight of Index System (e index weight reflects therelative importance of each index in the risk assessmentindex system In this paper the AHP (Analytic HierarchyProcess) and entropy weight are combined to calculate theweights of indices (e AHP is a subjective method to de-termine the weight of indices based on expertsrsquo experience[26] (e entropy weight method is an objective method thatdetermines the importance of index based on the data in-formation [27] (is combined method can reduce the in-terference of the individualrsquos subjective judgments on theweights of indices (erefore it can make the weights morerealistic and reliable relatively [28] (e idea of calculatingweights is to calculate the subjective weights of indices byAHP and then to establish the entropymodel of expertrsquos ownweights by using the subjective weights of indices as attributematrices through the idea of information entropy (eweights of the indices are corrected by the weights of theexperts and the final combination weights are obtained

Suppose the weights calculated by AHP are the sub-jective weights of indices (e subjective weights of indicescan be represented as follows

Wz wz1 wz2 wzr( 1113857T (1)

where Wz is the subjective weight vector 0ltwzs lt 1 and1113936

rs1 wzs 1 (s 1 2 r z 1 2 v)(en the expertrsquos own weights are calculated by the

entropy weight method and it can be calculated as follows

S S1 S2 Sv( 1113857T (2)

where the S is expertrsquos own weight vector 0lt Sz lt 1(z 1 2 v) and 1113936

vz1 Sz 1

Finally the weight fusion vector of subjective weightsand expertrsquos own weights can be expressed asW (w1 w2 wr)

T It can be calculated as follows

2 Mathematical Problems in Engineering

ws 1113944v

z1wzs times Sz( 1113857 (3)

where 0ltws lt 1 1113936rs1 ws 1 and s 1 2 r z

1 2 v

33 InformationDiffusionMethod (IDM) IDM is a kind of afuzzy mathematical processing method which can be used tooptimize the fuzzy information of samples by means of anappropriate diffusion model [29] When the sample is in-complete the method can make the diffusion estimationcloser to the real relationship than the nondiffusion esti-mation It can establish the level classification standards ofthe urban rainstorm disasters assessment indices to improvethe evaluation accuracy [30] Based on the historical data ofJiangsu province the different risk levels of indices weredetermined according to classification standards

(e principle of the information diffusionmethod can beexpressed as follows suppose X(X x1 x2 xn1113864 1113865) is asample and it can be used to estimate a relationship on adomain U When the X is incomplete there exist an ap-propriate diffusion function f(xi u) and the correspondingoperator cprime which can transform X into a fuzzy sampleD(X) thus the information with a value of 1 from thesample X can be diffused around the sample following thefunction f(xi u) and the diffusion estimate 1113957R is closer tothe real relationship than the nondiffusion estimate 1113954R

[31ndash33] It can be shown in Figure 2(e calculation steps of the information diffusion are as

followsFirstly the risk levels of urban rainstorm disasters are

divided into five levels ie lowest risk lower risk moderaterisk higher risk and highest risk(e information carried byxi(i 1 2 n) can be diffused into uj(j 1 2 m)

from the domain U according to the following equation

fi uj1113872 1113873 1

h2π

radic exp minusxi minus uj1113872 1113873

2h2⎡⎣ ⎤⎦ (4)

where h is the diffusion coefficient which can be calculatedby different sizes and the sample values

Secondly in order to make each set of sample valuesidentical the diffusion function fi(uj) is normalized to bethe diffusion function gi(uj)

gi uj1113872 1113873 fi uj1113872 1113873

1113936mj1 fi uj1113872 1113873

(5)

(e probability of samples located in uj can be repre-sented as follows

F uj1113872 1113873 1113936

ni1 gi uj1113872 1113873

1113936mj1 1113936

ni1 gi uj1113872 1113873

(6)

Finally the exceeding probability Flowast(uj) can be obtainedthrough the following equation

Flowast

uj1113872 1113873 1113944

m

kj

F uk( 1113857 (7)

XuzhouLianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N N

Figure 1 Geographic location of Jiangsu Province China

D (X)

γ

γprime

X

||R ndash R~|| lt ||R ndash R||

R (γ X)

R~ [γprime D (X)]

Figure 2 (e principle of the information diffusion method

Mathematical Problems in Engineering 3

According to the classification standards of exceedingprobability the critical value of each evaluation indexcorresponding to the risk level of urban rainstorm is ob-tained so the level classification standards of urban rain-storm risk assessment indices can be obtained

34 Variable Fuzzy Set (VFS) Variable fuzzy set theory ismainly used in the dynamic analysis of fuzzy phenomena[34] As its core relative membership function relativedifference function and variable fuzzy set quantify theprocess of changing things from quantity to quality anddescribe it with mathematical languages By changing themodel and model parameters the credibility and reliabilityof evaluation identification and decision-making can beincreased which provides new ideas for risk assessments inmany fields [35 36]

Fuzzy variable evaluation method calculates the evalu-ation level of urban rainstorm disasters scientifically bychanging the model and its parameter combination and itcan improve the reliability of risk assessment results (efuzzy variable evaluation method mainly includes the fol-lowing steps

(1) Generating index eigenvalue matrixSuppose there is a sample set Y y1 y2 middot middot middot yn1113864 1113865

consisting of n samples of natural disasters (eindex eigenvalue of the sample i can be expressed asyi (y1i y2i yri)

T where r is the number of

sample indices (en the sample set can be expressas Y (ysi)rtimesn where s 1 2 r i 1 2 n

(2) Establishing index standard eigenvalue matrixSuppose there are m levels of assessment classi-fication standards And the sample set is identi-fied according to different standards ofeigenvalues of r sample indices and then thestandard eigenvalue matrix of the first order in-dex is obtained

(3) Calculating the relative membership matrix of indexlevel(e interval matrix and the bound matrix of variableset of indices can be determined by referring to thestandard value matrix of indices and the actualsituation of the area (en according to the differenteigenvalues of samples ysi the different degreeLA(ysi) the relative membership degree matrix canbe calculated as follows

μA ysi( 1113857 1 + LA ysi( 1113857

2 (8)

(4) Determining the weight of each index and the rel-ative membership degree

According to equation (8) the nonnormalized relativemembership degree can be calculated as follows

Tlowasthi 1 +

1113936rs1 ws 1 minus μA ysi( 1113857( 11138571113858 1113859

1113936rs1 wsμA ysi( 11138571113858 1113859

P⎡⎣ ⎤⎦

αP⎧⎨

⎩

⎫⎬

⎭

minus 1

(9)

where ws(s 1 2 r) is the index weight that can becalculated by equation (3) And r is the identify number ofindices h is the risk level number where h 1 2 m α isthe optimal rule parameter (α 1 2) P is the distanceparameter P 1 is Hamming distance and P 2 is Eu-clidean distance (en the normalized relative membershipdegree can be calculated as follows

Thi

Tlowasthi

1113936mh1 Tlowasthi

(10)

Finally according to the principle of the largest degree ofmembership we can obtain the risk levels of urban rain-storm disasters

4 Results and Analysis

41 Risk Index System of Urban Rainstorm In this paper anintegrated risk assessment index system of urban rainstormdisasters was established (see Table 1) (e index system isdivided into three subsystems including dangerousnesssensitivity and vulnerability

(e dangerousness indices reflect the abnormal condi-tions and factors of external natural environment(e risk ofurban rainstorm disasters can be attributed to short-termrainfall far exceeding normal situations or long-term rainfallin cities which will lead to the arranged discharge ofrainwater beyond the capacity of urban drainage networkGenerally the larger the dangerousness is the higher the riskof urban rainstorm disasters is (is paper chooses con-tinuous rainfall days (I11 days) heavy rain days (I12 days)maximum rainfall in 24 h (I13 mm) monthly total rainfall(I14 mm) and precipitation anomaly percentage (I15 ) asthe evaluation indices of dangerousness

(e sensitivity indices represent that a particular regionis potential to the destruction and influence of disasters dueto various natural and social factors [37] Jiangsu Province islocated in the plain area with low altitude and it is easy tocause floods once it encounters rainstorms And rapid ur-banization leads to the change of urban surface attributes(e urban surface is mostly impervious and hardenedsurface which leads to the rapid convergence of surfacerainwater under extreme rainstorms (e construction ofdrainage pipeline network in cities also has not kept pacewith the development of the city So the urban averageelevation (I21 m) urban green coverage rate (I22 ) urbandrainage network density (I23 kmkm2) urban water areapercentage (I24 ) and impermeable construction land (I25km2) were selected as the sensitivity indices

(e vulnerability indices describe the potential losses ofthe area exposed to the risk [38] It refers to the possibleimpact of urban rainstorm disasters in the urban that is thelevel of loss caused by urban rainstorm It is generally be-lieved that densely populated industrially developed citiessuffer greater risks and losses in face of the urban rainstormdisasters (e vulnerability indices include the density of


affected population (I31 peoplekm2) GDP of unit area (I32100 million yuankm2) disaster relief investment level (I33) and public emergency response capability (I34) (epublic emergency response capability (I34) can be quantifiedby expert scoring Some experts are asked to score the indexand the average score is calculated as the index value

42 Risk Evaluation Based on IDM-VFSModel Based on therisk assessment index system and IDM-VFS model firstlythe AHP was combined with the entropy method to de-termine the weights of the risk indices of urban rainstormdisasters secondly the IDM was adopted to determine theclassification standards of the risk indices thirdly the di-saster risk values in dangerousness sensitivity and vul-nerability can be calculated by the VFS model respectivelyFinally the comprehensive disaster risk levels were obtainedand the risk zoning map was drawn

421 Determination of the Weights of Risk Indices Indexweights are determined by combined AHP and the entropyweight method (e weights of risk assessment indices areshown in Table 2

422 Calculation of the Level Classification Standards ofIndices (e level classification standards of each risk as-sessment index of urban rainstorm disasters are determinedby IDM Firstly the index values can be taken as samples ofinformation diffusion then the exceeding probability of eachindex also can be calculated Finally the level classificationstandards of each risk assessment secondary index are ob-tained (see Table 3)

423 Calculation of the Disaster Risks of gtree SubsystemsAccording to Table 3 the interval matrix and bound matrixare established then based on equations (8)ndash(10) the rel-ative membership degree matrix and integrated membershipdegree are obtained and finally the risk values can becalculated in terms of dangerousness sensitivity and vul-nerability respectively For demonstration purposesNanjing has been chosen as an example to discuss the risk

assessment of urban rainstorm disasters in detail (e riskvalues of urban rainstorm disasters in Nanjing in 2016 areshown in Table 4

From Table 4 dangerousness indices have different riskvalues due to different monthly rainfall and rainfall daysSensitivity and vulnerability indices remain basically un-changed in one year while the urban average elevationurban green coverage rate and impermeable constructionland can be changed in a period time So sensitivity andvulnerability indices could change in many years Adoptingthe same methods and procedures we can obtain risk valuesof the urban rainstorm in Nanjing from 2010 to 2016 interms of dangerousness sensitivity and vulnerability re-spectively (see Table 5)

From Table 5 the average risk value of dangerousness is348 the average risk value of sensitivity is 372 and theaverage risk value of vulnerability is 282(e dangerousnessof 2011 is higher than other years because the precipitation

Table 1 Risk assessment index system of urban rainstorm disaster

Target layer Primary indices Secondary indices

Urban rainstorm disaster risk

Dangerousness

Continuous rainfall days (I11 days) (monthly)Heavy rain days (I12 days) (monthly)Maximum rainfall in 24 h (I13 mm)Monthly total rainfall (I14 mm)

Precipitation anomaly percentage (I15 )

Sensitivity

Urban average elevation (I21 m)Urban green coverage rate (I22 )

Urban drainage network density (I23 kmkm2)Urban water area percentage (I24 )

Impermeable construction land (I25 km2)

Vulnerability

Density of affected population (I31 Peoplekm2)GDP of unit area (I32 100 million yuankm2)

Disaster relief investment level (I33 )Public emergency response capability (I34)

Table 2 (e index weights of urban rainstorm disasters

Primaryindices Secondary indices Weight

ωi

Dangerousness

Continuous rainfall days (days) 00630Heavy rain days (days) 00735

Maximum rainfall in 24 h (mm) 00945Monthly total rainfall (mm) 00665

Precipitation anomaly percentage () 00425

Sensitivity

Urban average elevation (m) 00772Urban green coverage rate () 00577Urban drainage network density

(kmkm2) 00927

Urban water area percentage () 00735Impermeable construction land (km2) 01279

Vulnerability

Density of affected population (peoplekm2) 00424

GDP of unit area (100 million yuankm2) 00916

Disaster relief investment level () 00452Public emergency response capability 00408


was higher than the average level (Figure 3(a)) According tohistorical data collected from the meteorological stations ofcities in Jiangsu Province it had sustained rainfall and strongrainfall intensity in short duration Blanc et al [10] showedthat intense direct rainfall can overwhelm urban drainagesystems and cause complex and often localised patterns ofpluvial flooding In terms of statistic the weight and relativemembership degree of maximum rainfall in 24 h and heavyrain days are higher than other dangerousness indicationsSo ldquosustained rainfall and strong rainfall intensity in shortdurationrdquo is the main reason for affecting dangerousnessDue to the acceleration of urbanization the reduction ofgreen area cause impermeable construction land increase sothe sensitivity and vulnerability had an upward tendency(Figures 3(b) and 3(c)) While the impervious constructionarea of the cities the density of the affected population andGDP are gradually increasing from 2010 to 2016 thedrainage facilities and greening constructions are notgrowing responsively

By calculating the average risk values in different cities interms of dangerousness sensitivity and vulnerability therisk levels of urban rainstorm disasters are shown in Table 6

(e dangerousness of Wuxi Changzhou Nanjing andSuzhou is higher while that of Xuzhou Huairsquoan and Suqianis lower from 2010 to 2016 (e major influence factors ofdangerousness are sustained rainfall and strong rainfallintensity in short duration And the precipitation decreasedfrom south to north gradually

(e sensitivity of Wuxi Changzhou and Nanjing ishigher while that of Xuzhou and Suqian is lower (esensitivity of urban rainstorm disasters mainly depends onthe natural and social environment of the cities and thedisaster resistance level (e conditions of the different citiesin Jiangsu Province are uneven Because different cities havedifferent natural and social environments ZhenjiangXuzhou and Suqian have higher altitudes and less imper-vious construction area which makes them have lowersensitivity Wuxi Changzhou and Nanjing are more ad-vanced so they have more impervious construction areawhich decreased the ability of disaster resistance resulting inhigher sensitivity [39]

Lianyungang Yancheng and Suqian are located in thelowest vulnerability area while Wuxi and Suzhou are thehighest cities (e vulnerability of urban rainstorm is thereflection of the vulnerable degree of social economy andhuman society capability to disasters Lianyungang Yan-cheng and Suqian have lower GDP of unit area and affectedpopulation density so they belong to the lower disastervulnerability cities (e GDP of unit area in Wuxi andSuzhou is more than 500 million (yuankm2) and thepopulation density is higher leading to the highest urbanrainstorm vulnerability Hurlbert and Dhakal [40 41] bothconsidered social economy and human society capability todisasters are the main reason for affecting vulnerability

(e comparisons of different cities in terms of dan-gerousness sensitivity and vulnerability respectively inJiangsu Province are shown in Figure 4

Based on the assessment results the comprehensive riskzoning map in Jiangsu Province can be drawn (Figure 5)From Figure 5 it can be seen that the comprehensive risks of

Table 3 Level classification standards of risk assessment secondary indices

Secondary indices First level(lowest)

Second level(lower)

(ird level(moderate)

Forth level(higher)

Fifth level(highest)

Continuous rainfall days (days) lt1 1sim2 2sim4 4sim6 gt6Heavy rain days (days) lt1 1sim3 3sim5 5sim7 gt7Maximum rainfall in 24h (mm) lt25 25sim50 50sim100 100sim200 gt200Monthly total rainfall (mm) lt50 50sim124 124sim236 236sim378 gt378Precipitation anomaly percentage () lt4 4sim15 15sim40 40sim100 gt100Urban average elevation (m) gt35 35sim20 20sim10 10sim5 lt5Urban green coverage rate () gt50 50sim40 40sim30 30sim20 lt20Urban drainage network density (kmkm2) gt32 32sim24 24sim16 16sim10 lt10Urban water area percentage () gt30 30sim20 20sim15 15sim10 lt10Impermeable construction land (km2) lt90 90sim148 148sim245 245sim440 gt440Density of affected population (peoplekm2) lt1265 1265sim2355 2355sim3375 3375sim4430 gt4430GDP of unit area (100 million yuankm2) lt08 08sim12 12sim3 3sim5 gt5Disaster relief investment level () gt17 15sim17 13sim15 9sim13 lt9Public emergency response capability gt90 90sim80 80sim70 70sim60 lt60

Table 4 Risk values from June to August in 2016 in Nanjing

Month Dangerousness Sensitivity VulnerabilityJune 411 382 287July 421 382 285August 246 382 288Average 359 382 287

Table 5 Risk values of urban rainstorm from 2010 to 2016 inNanjing

Year Dangerousness Sensitivity Vulnerability2010 344 364 2782011 375 366 2782012 346 368 2812013 332 368 2762014 321 373 2842015 361 379 2862016 359 382 287Average 348 372 282


283032343638

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(a)

27

275

28

285

29

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(b)

350355360365370375380385

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(c)

Figure 3 Variation tendency of risk values in terms of three subsystems from 2010 to 2016 (a) dangerousness (b) sensitivity(c) ulnerability

Table 6 Comprehensive risk level in Jiangsu Province

City Dangerousness Sensitivity Vulnerability Risk levelNanjing 348 372 282 4Wuxi 367 346 337 4Xuzhou 247 239 253 2Changzhou 362 358 303 4Suzhou 353 307 326 3Nantong 328 271 308 3Lianyungang 281 298 233 3Yancheng 283 274 246 3Yangzhou 302 245 287 3Zhenjiang 317 234 306 3Taizhou 286 257 294 3Huairsquoan 257 269 266 3Suqian 263 208 247 2

0

05

1

15

2

25

3

35

4

Risk

val

ues

City

Nan

jing

Wux

i

Xuzh

ou

Chan

gzho

u

Suzh

ou

Nan

tong

Lian

yung

ang

Yanc

heng

Yang

zhou

Zhen

jiang

Taiz

hou

Hua

irsquoan

Suqi

an

DangerousnessSensitivityVulnerability

Figure 4 Comparisons of different cities in terms of dangerousness sensitivity and vulnerability respectively in Jiangsu Province


urban rainstorm in Jiangsu Province have apparent regionalcharacteristics (e comprehensive risk levels of urbanrainstorm in Wuxi Changzhou and Nanjing are higherwhile those of Xuzhou and Suqian are lower(e risk levels inthe northwestern cities are lower than the southern cities inthe Jiangsu Province Moreover most cities of JiangsuProvince experience moderate risk level

5 Discussion and Conclusions

Urban rainstorm risk assessment involves many factors thusthis paper established an integrated index system in termsof dangerousness of hazard-formative factors sensitivity ofhazard-inducing environments and vulnerability of hazard-affected body (en the IDM and VFS models were coupled toassess the comprehensive risk of the urban rainstorm In thecoupled model the IDM was adopted to determine theclassification standards of the VFS (e assessment results ofJiangsu Province showed thatmost cities are at themoderate risklevel and the northwestern cities have lower risk than southerncities In the dangerousness subsystem due to the heavy rainfallin short-term Wuxi Changzhou Nanjing and Suzhou havehigher risk than Xuzhou Huairsquoan and Suqian from 2010 to2016 In the sensitivity subsystem because of low urban rain-storm resistance capability Wuxi Changzhou and Nanjinghave higher risk than other cities In the vulnerability subsystemWuxi and Suzhou have higher risk while Liangyungang Yan-chang and Suqian have lower risk (e assessment results canhelp the local government to improve the rainstorm resistancecapability and reduce the losses caused by rainstorm disasters

In this paper Jiangsu Province is a typical city sufferingfrom frequent urban rainstorm disasters in recent yearsAccording to the characteristics of regional urban rainstormdisasters the risk assessment index system of urban rain-storm disasters is constructed Based on the IDM and VFSmodel the risk assessment model is established to assess therisk of rain and flood disasters in 13 cities of JiangsuProvince from 2010 to 2016 (en according to the as-sessment results the risk map of urban rainstorm disaster isdrawn by ArcGIS and the assessment results are analyzedFinally the corresponding control measures are put forwardwhich can provide decision-making reference for JiangsuProvince and other cities

Data Availability

(e continuous rainfall days heavy rain days maximumrainfall in 24 h monthly total rainfall precipitation anomalypercentage the urban average elevation urban green cov-erage rate urban drainage network density urban water areapercentage impermeable construction land the density ofaffected population GDP of unit area disaster relief in-vestment level and public emergency response capabilitydata used to support the findings of this study are availablefrom the corresponding author upon request

Conflicts of Interest

(e authors declare that there are no conflicts of interestregarding the publication of the paper

Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade

Figure 5 Distribution of urban rainstorm risk in Jiangsu Province


Acknowledgments

(is research was supported by the National Key Researchand Development Program of China (grant no2019YFC0409000) the National Natural Science Foundationof China (grant no 41877526) the Fundamental ResearchFunds for the Central Universities (grant no B200204018)the Water Conservancy Science and Technology Project ofJiangsu Province (grant no 2017060) and the Humanitiesand Social Sciences Fund of Ministry of Education of China(grant no 18YJA630009)

References

[1] X H He andW F Hu ldquoA two-stage queue model to optimizelayout of urban drainage system considering extreme rain-stormsrdquo Mathematical Problems in Engineering vol 2017Article ID 6380521 18 pages 2017

[2] S H A Koop and C J Van Leeuwen ldquo(e challenges ofwater waste and climate change in citiesrdquo EnvironmentDevelopment and Sustainability vol 19 no 2 pp 385ndash4182017

[3] A Jerneck and L Olsson ldquoStructuring sustainability sciencerdquoSustainability Science vol 6 no 1 pp 69ndash82 2011

[4] D L T Hegger P P J Driessen C Dieperink M WieringG T T Raadgever and H F M W Van Rijswick ldquoAssessingstability and dynamics in flood risk governancerdquo WaterResources Management vol 28 no 12 pp 4127ndash4142 2014

[5] R Quan ldquoRisk assessment of flood disaster in Shanghai basedon spatial-temporal characteristics analysis from 251 to 2000rdquoEnvironmental Earth Sciences vol 72 no 11 pp 4627ndash46382014

[6] Z Huang J Zhou L Song Y Lu and Y Zhang ldquoFlooddisaster loss comprehensive evaluation model based on op-timization support vector machinerdquo Expert Systems withApplications vol 37 no 5 pp 3810ndash3814 2010

[7] S M Liu H Wang D Yan Q Ren D Wang and B GongldquoAnalysis of spatiotemporal evolution of isolated rainstormevents in Huai river basin Chinardquo Advances in Meteorologyvol 2017 Article ID 3010295 17 pages 2017

[8] D Zhang and L Wang ldquoResearch on urban emergencymanagement in Beijing based on complex system theoryrdquoCity vol 4 pp 49ndash53 2016

[9] H-M Lyu W-J Sun S-L Shen and A Arulrajah ldquoFloodrisk assessment in metro systems of mega-cities using a GIS-based modeling approachrdquo Science of the Total Environmentvol 626 pp 1012ndash1025 2018

[10] M I Alfa M A Ajibike and R E Daffi ldquoApplication ofanalytic hierarchy process and geographic information systemtechniques in flood risk assessment a case of Ofu rivercatchment in Nigeriardquo Journal of Degraded and Mining LandsManagement vol 5 no 4 pp 1363ndash1372 2018

[11] K M Weerasinghe H Gehrels N M S I ArambepolaH P Vajja J M K Herath and K B Atapattu ldquoQualitativeflood risk assessment for the Western Province of Sri LankardquoProcedia Engineering vol 212 pp 503ndash510 2018

[12] J F Chen Q Li H M Wang and M H Deng ldquoA machinelearning ensemble approach based on random forest andradial basis function neural network for risk evaluation ofregional flood disaster a case study of the Yangtze river DeltaChinardquo International Journal of Environmental Research andPublic Health vol 17 no 1 p 49 2019

[13] J Wang L Zhao H Zhang and W Niu ldquoResearch onoptimization of urban drainage pipelinesrsquo carrying capacitybased on SWMM modelrdquo China Rural Water and Hydro-power vol 4 pp 41ndash44 2017

[14] M Shao Z Gong and X Xu ldquoRisk assessment of rainstormand flood disasters in China between 2004 and 2009 based ongray fixed weight cluster analysisrdquo Natural Hazards vol 71no 2 pp 1025ndash1052 2014

[15] J N Goetz R H Guthrie and A Brenning ldquoForest harvesting isassociated with increased landslide activity during an extremerainstorm on Vancouver Island Canadardquo Natural Hazards andEarth System Sciences vol 15 no 6 pp 1311ndash1330 2015

[16] H-M Lyu G-F Wang W-C Cheng and S-L ShenldquoTornado hazards on June 23 in Jiangsu Province Chinapreliminary investigation and analysisrdquo Natural Hazardsvol 85 no 1 pp 597ndash604 2017

[17] M C Strzelecki A J Long and J M Lloyd ldquoPost-little ice agedevelopment of a high arctic paraglacial beach complexrdquoPermafrost and Periglacial Processes vol 28 no 1 pp 4ndash172017

[18] X Liu X Li and S Dang ldquoSpatial pattern of precipitationchange in the main sediment-yielding area of the Yellow riverbasin in recent yearsrdquo Journal of Hydraulic Engineeringvol 47 pp 463ndash472 2016

[19] J Li S Tan Z Wei F Chen and P Feng ldquoA new method ofchange point detection using variable fuzzy sets under en-vironmental changerdquo Water Resources Management vol 28no 14 pp 5125ndash5138 2014

[20] Y F Ren G D Liu L Zhou and C Zhang ldquoRisk evaluationof Chengdursquos flood hazard based on evidence theory andvariable fuzzy sets theoryrdquo Transactions of the Chinese Societyof Agricultural Engineering vol 30 no 21 pp 147ndash156 2014in Chinese

[21] P Wang ldquoResearch on the flood risk assessment of Guizhoubased on information diffusion theory and data integrationrdquoChina Rural Water and Hydropower vol 2 pp 109ndash112 2018in Chinese

[22] L J Zou M Zhong X H Yang and X F Liu ldquoUsing in-formation diffusion to analyze the membership degree in riskassessment of flash floodrdquo Journal of Water Resources Re-search vol 5 pp 598ndash604 2016

[23] E Guo J Zhang X Ren Q Zhang and Z Sun ldquoIntegratedrisk assessment of flood disaster based on improved set pairanalysis and the variable fuzzy set theory in central LiaoningProvince Chinardquo Natural Hazards vol 74 no 2 pp 947ndash965 2014

[24] J Chen M Deng L Xia and H Wang ldquoRisk assessment ofdrought based on IDM-VFS in the Nanpan river basinYunnan Province Chinardquo Sustainability vol 9 no 7 p 11242017

[25] X Du X Jin X Yang X Yang and Y Zhou ldquoSpatial patternof land use change and its driving force in Jiangsu provincerdquoInternational Journal of Environmental Research and PublicHealth vol 11 no 3 pp 3215ndash3232 2014

[26] T L Saaty ldquoDecision-making with the AHP why is theprincipal eigenvector necessaryrdquo European Journal of Oper-ational Research vol 145 no 1 pp 85ndash91 2003

[27] D Zhao Y Zhang and J Ma ldquoFuzzy risk assessment ofentropy-weight coefficient method applied in network secu-rityrdquo Computer Engineering vol 30 pp 21ndash23 2004

[28] J Xu P Feng and P Yang ldquoResearch of development strategyon Chinarsquos rural drinking water supply based on SWOT-TOPSIS method combined with AHP-Entropy a case in


Hebei Provincerdquo Environmental Earth Sciences vol 75 no 1p 58 2016

[29] L J Zhang W Li and D Y Zhang ldquoMeteorological disasterrisk assessment method based on information diffusiontheoryrdquo Scientia Geographica Sinica vol 29 pp 250ndash2542009

[30] J D Wang and C F Huang ldquoInformation diffusion methodrelevant in fuzzy information processing and its applicationrdquoJournal of Northwest University vol 22 pp 383ndash392 1992

[31] C F Huang ldquoPrinciple of information diffusionrdquo Fuzzy Setsand Systems vol 91 no 1 pp 69ndash90 1997

[32] Q Li ldquoFlood risk assessment based on the Information dif-fusion methodrdquo in Proceedings of the Advances in ComputerScience Environment Ecoinformatics and EducationSpringer Berlin Heidelberg German pp 111ndash117 August2011

[33] K Nagata and S Shirayama ldquoMethod of analyzing the in-fluence of network structure on information diffusionrdquoPhysica A Statistical Mechanics and its Applications vol 391no 14 pp 3783ndash3791 2012

[34] S Y Chen ldquo(eory and model of engineering variable fuzzyset-Mathematical basis for fuzzy hydrology and water re-sourcesrdquo Journal of Dalian University of Technology vol 45pp 308ndash312 2005

[35] S Huang J Chang G Leng and Q Huang ldquoIntegrated indexfor drought assessment based on variable fuzzy set theory acase study in the Yellow river basin Chinardquo Journal of Hy-drology vol 527 pp 608ndash618 2015

[36] H C Zhou and Z Dan ldquoAssessment model of drought andflood disasters with variable fuzzy set theoryrdquo Transactions ofthe Chinese Society of Agricultural Engineering vol 25pp 56ndash61 2009

[37] F Denton ldquoClimate change vulnerability impacts and ad-aptation why does gender matterrdquo Gender amp Developmentvol 10 no 2 pp 10ndash20 2002

[38] G Hufschmidt ldquoA comparative analysis of several vulnera-bility conceptsrdquo Natural Hazards vol 58 no 2 pp 621ndash6432011

[39] J H Danumah S N Odai B M Saley et al ldquoFlood riskassessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation tech-niques (Cote drsquoIvoire)rdquo Geoenvironmental Disasters vol 3no 1 p 10 2016

[40] M Hurlbert and J Gupta ldquo(e adaptive capacity of insti-tutions in Canada Argentina and Chile to droughts andfloodsrdquo Regional Environmental Change vol 17 no 3pp 865ndash877 2017

[41] K P Dhakal and L R Chevalier ldquoManaging urban storm-water for urban sustainability barriers and policy solutionsfor green infrastructure applicationrdquo Journal of Environ-mental Management vol 203 pp 171ndash181 2017


sensitivity of hazard-inducing environments and vulnera-bility of hazard-affected body

Meanwhile GIS (the Geographic Information System)and remote-sensing imagery [15ndash18] are adopted to assessthe risk assessment of urban rainstorm disasters Howeverdue to lack of sufficient data urban rainstorm disasters aredifficult to assess accurately [19] (erefore fuzzy and un-certainty theories were introduced into the risk assessmentof urban rainstorm disasters Ren [20] adopted variablefuzzy sets to assess the flood risk in Chengdu China Wang[21] evaluated the flood risk in Guizhou based on the in-formation diffusion method Zou [22] adopted informationdiffusion to analyze the flood risk However there are somedrawbacks in the above methods For example the infor-mation diffusionmethod (IDM) cannot assess the subsystemrisk in the rainstorm assessment while the variable fuzzy sets(VFSs) are difficult to determine the classification standardsobjectively [23] Chen [24] showed that the IDM can help theVFS to establish the classification standards and the VFS canobtain the subsystems and comprehensive risk evaluationresults (erefore in this paper a brand-new domain ldquotherisk of urban rainstorm disastersrdquo is studied We build a newevaluation index system in this paper Urban rainstorm is afuzzy phenomenon and lack of sufficient data traditionalstatistics are difficult to depict it accurately especially insmall sample problems (erefore some fuzzy and uncer-tainty theories have been developed to assess the risk ofurban rainstorm disasters(e variable fuzzy sets (VFSs) canmake full use of various index data to obtain comprehensiverisk evaluation results (e information diffusion method(IDM) can transform a sample observed value into a fuzzyset and is capable of dealing with small sample problemsMeanwhile IDM can extract useful information and es-tablish the level classification standards of urban rainstormdisasters risk assessment indices which is helpful to de-termine the relative membership function of the VFS(erefore in this research the level classification standardsof each risk assessment index of urban rainstorm disasterswere calculated by the IDM and then risks of dangerous-ness sensitivity and vulnerability were obtained by the VFSFinally Jiangsu Province in China was taken as a case studyarea

2 Overview of Study Area

(is paper takes Jiangsu Province as the study area JiangsuProvince (Figure 1) is located between 116deg18primeEndash121deg57primeEand 30deg45primeNndash35deg20primeN (e total area is 1072times105 km2accounting for 11 area of China (e geomorphology ofJiangsu is mainly plains and the elevation of most area islower than 50m [25] (ere are 13 prefecture-level cities inJiangsu Province including Nanjing (provincial capital city)Wuxi Xuzhou Changzhou Suzhou Nantong Lia-nyungang Huairsquoan Yancheng Yangzhou ZhenjiangTaizhou and Suqian Jiangsu Province belongs to the sub-tropical monsoon climate and thus most of the large-scaleprecipitation is happed in summer To better show the actualsituations of the urban rainstorm disasters of Jiangsu

Province this paper emphasizes research the period fromJune to August

Jiangsu Province is one of the most urbanized regions inChina In recent years urban rainstorm disasters have oc-curred more frequently and caused huge losses in JiangsuProvince(us urban rainstorm has become one of the mostimportant factors that restrict the development of JiangsuProvince

3 Data and Methods

31 Data Sources (is paper uses Jiangsu Statistical Year-book data and China City Statistical Yearbook data to collectthe statistical information of all cities in Jiangsu Provinceincluding economic social demographic urban construc-tion environmental and other related city statistics (emeteorological data and the rainfall statistics are collectedfrom the meteorological stations of cities in Jiangsu Prov-ince Other historical precipitation statistics are provided bythe Jiangsu climate center Among these data the contin-uous rainfall days and heavy rain days are collected bymonthly from 2010 to 2016 from the Jiangsu climate center

32 Weight of Index System (e index weight reflects therelative importance of each index in the risk assessmentindex system In this paper the AHP (Analytic HierarchyProcess) and entropy weight are combined to calculate theweights of indices (e AHP is a subjective method to de-termine the weight of indices based on expertsrsquo experience[26] (e entropy weight method is an objective method thatdetermines the importance of index based on the data in-formation [27] (is combined method can reduce the in-terference of the individualrsquos subjective judgments on theweights of indices (erefore it can make the weights morerealistic and reliable relatively [28] (e idea of calculatingweights is to calculate the subjective weights of indices byAHP and then to establish the entropymodel of expertrsquos ownweights by using the subjective weights of indices as attributematrices through the idea of information entropy (eweights of the indices are corrected by the weights of theexperts and the final combination weights are obtained

Suppose the weights calculated by AHP are the sub-jective weights of indices (e subjective weights of indicescan be represented as follows

Wz wz1 wz2 wzr( 1113857T (1)

where Wz is the subjective weight vector 0ltwzs lt 1 and1113936

rs1 wzs 1 (s 1 2 r z 1 2 v)(en the expertrsquos own weights are calculated by the

entropy weight method and it can be calculated as follows

S S1 S2 Sv( 1113857T (2)

where the S is expertrsquos own weight vector 0lt Sz lt 1(z 1 2 v) and 1113936

vz1 Sz 1

Finally the weight fusion vector of subjective weightsand expertrsquos own weights can be expressed asW (w1 w2 wr)

T It can be calculated as follows


ws 1113944v



1 2 v







fi uj1113872 1113873 1

h2π


2h2⎡⎣ ⎤⎦ (4)



gi uj1113872 1113873 fi uj1113872 1113873

1113936mj1 fi uj1113872 1113873

(5)


F uj1113872 1113873 1113936

ni1 gi uj1113872 1113873

1113936mj1 1113936

ni1 gi uj1113872 1113873

(6)


Flowast

uj1113872 1113873 1113944

m

kj

F uk( 1113857 (7)

XuzhouLianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N N


D (X)

γ

γprime

X


R (γ X)

R~ [γprime D (X)]












μA ysi( 1113857 1 + LA ysi( 1113857

2 (8)



Tlowasthi 1 +

1113936rs1 ws 1 minus μA ysi( 1113857( 11138571113858 1113859

1113936rs1 wsμA ysi( 11138571113858 1113859

P⎡⎣ ⎤⎦

αP⎧⎨

⎩

⎫⎬

⎭

minus 1

(9)


Thi

Tlowasthi


(10)



















Dangerousness



Sensitivity




Vulnerability





ωi

Dangerousness




Sensitivity


(kmkm2) 00927


Vulnerability














Second level(lower)


Forth level(higher)








283032343638

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(a)

27

275

28

285

29

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(b)

350355360365370375380385

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(c)




0

05

1

15

2

25

3

35

4

Risk

val

ues

City

Nan

jing

Wux

i

Xuzh

ou

Chan

gzho

u

Suzh

ou

Nan

tong

Lian

yung

ang

Yanc

heng

Yang

zhou

Zhen

jiang

Taiz

hou

Hua

irsquoan

Suqi

an








Data Availability




Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade



Acknowledgments


References













































ws 1113944v



1 2 v







fi uj1113872 1113873 1

h2π


2h2⎡⎣ ⎤⎦ (4)



gi uj1113872 1113873 fi uj1113872 1113873

1113936mj1 fi uj1113872 1113873

(5)


F uj1113872 1113873 1113936

ni1 gi uj1113872 1113873

1113936mj1 1113936

ni1 gi uj1113872 1113873

(6)


Flowast

uj1113872 1113873 1113944

m

kj

F uk( 1113857 (7)

XuzhouLianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N N


D (X)

γ

γprime

X


R (γ X)

R~ [γprime D (X)]












μA ysi( 1113857 1 + LA ysi( 1113857

2 (8)



Tlowasthi 1 +

1113936rs1 ws 1 minus μA ysi( 1113857( 11138571113858 1113859

1113936rs1 wsμA ysi( 11138571113858 1113859

P⎡⎣ ⎤⎦

αP⎧⎨

⎩

⎫⎬

⎭

minus 1

(9)


Thi

Tlowasthi


(10)



















Dangerousness



Sensitivity




Vulnerability





ωi

Dangerousness




Sensitivity


(kmkm2) 00927


Vulnerability














Second level(lower)


Forth level(higher)








283032343638

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(a)

27

275

28

285

29

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(b)

350355360365370375380385

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(c)




0

05

1

15

2

25

3

35

4

Risk

val

ues

City

Nan

jing

Wux

i

Xuzh

ou

Chan

gzho

u

Suzh

ou

Nan

tong

Lian

yung

ang

Yanc

heng

Yang

zhou

Zhen

jiang

Taiz

hou

Hua

irsquoan

Suqi

an








Data Availability




Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade



Acknowledgments


References






















































μA ysi( 1113857 1 + LA ysi( 1113857

2 (8)



Tlowasthi 1 +

1113936rs1 ws 1 minus μA ysi( 1113857( 11138571113858 1113859

1113936rs1 wsμA ysi( 11138571113858 1113859

P⎡⎣ ⎤⎦

αP⎧⎨

⎩

⎫⎬

⎭

minus 1

(9)


Thi

Tlowasthi


(10)



















Dangerousness



Sensitivity




Vulnerability





ωi

Dangerousness




Sensitivity


(kmkm2) 00927


Vulnerability














Second level(lower)


Forth level(higher)








283032343638

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(a)

27

275

28

285

29

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(b)

350355360365370375380385

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(c)




0

05

1

15

2

25

3

35

4

Risk

val

ues

City

Nan

jing

Wux

i

Xuzh

ou

Chan

gzho

u

Suzh

ou

Nan

tong

Lian

yung

ang

Yanc

heng

Yang

zhou

Zhen

jiang

Taiz

hou

Hua

irsquoan

Suqi

an








Data Availability




Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade



Acknowledgments


References
























































Dangerousness



Sensitivity




Vulnerability





ωi

Dangerousness




Sensitivity


(kmkm2) 00927


Vulnerability














Second level(lower)


Forth level(higher)








283032343638

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(a)

27

275

28

285

29

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(b)

350355360365370375380385

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(c)




0

05

1

15

2

25

3

35

4

Risk

val

ues

City

Nan

jing

Wux

i

Xuzh

ou

Chan

gzho

u

Suzh

ou

Nan

tong

Lian

yung

ang

Yanc

heng

Yang

zhou

Zhen

jiang

Taiz

hou

Hua

irsquoan

Suqi

an








Data Availability




Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade



Acknowledgments


References






















































Second level(lower)


Forth level(higher)








283032343638

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(a)

27

275

28

285

29

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(b)

350355360365370375380385

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(c)




0

05

1

15

2

25

3

35

4

Risk

val

ues

City

Nan

jing

Wux

i

Xuzh

ou

Chan

gzho

u

Suzh

ou

Nan

tong

Lian

yung

ang

Yanc

heng

Yang

zhou

Zhen

jiang

Taiz

hou

Hua

irsquoan

Suqi

an








Data Availability




Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade



Acknowledgments


References













































283032343638

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(a)

27

275

28

285

29

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(b)

350355360365370375380385

2010 2011 2012 2013 2014 2015 2016

Risk

val

ues

Year

(c)




0

05

1

15

2

25

3

35

4

Risk

val

ues

City

Nan

jing

Wux

i

Xuzh

ou

Chan

gzho

u

Suzh

ou

Nan

tong

Lian

yung

ang

Yanc

heng

Yang

zhou

Zhen

jiang

Taiz

hou

Hua

irsquoan

Suqi

an








Data Availability




Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade



Acknowledgments


References

















































Data Availability




Xuzhou

Lianyungang

Suzhou

Huairsquoan

Taizhou

ZhenjiangNantong

Yangzhou

Yancheng

Nanjing

Wuxi

Changzhou

Suqian

0 25 50 100 150 200miles

N

123

45

Grade



Acknowledgments


References













































Acknowledgments


References













































UsingMultipleIndexComprehensiveMethodtoAssessUrban...

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Transcript of UsingMultipleIndexComprehensiveMethodtoAssessUrban...