Skip to main content

Advertisement

SpringerLink
Log in

Flood disaster resilience evaluation of Chinese regions: integrating the hesitant fuzzy linguistic term sets with prospect theory

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

The aggravation of flood risk has been regarded as a serious threat to the natural ecological environment and the development of human society worldwide. There is a large population living on the banks of rivers, lakes and other flood plains. Since the introduction of the concept of disaster resilience, it has developed rapidly and has been widely applied in the field of disaster management. We introduce a new method by taking prospect theory as the main idea and incorporating the hesitant fuzzy linguistic term sets into the evaluation process. We illustrate its application through a case study of the provincial-level regions along the Yangtze River Basin. We find that the flood resilience in the west is generally stronger than that in the east. The strongest one is in Yunnan due to its unique natural environmental advantages while the weakest one is in Jiangxi because of its poor and immature natural, social, economic and management performance. We put forward specific management insights that consider different levels of resilience and the actual situation in each region.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Aerts JCJH, Botzen WJW, Emanuel K, Lin N, De Moel H, Michel KEO (2014) Evaluating flood resilience strategies for coastal megacities. Science 344(6183):473–475

    Article  Google Scholar 

  • Aguilar BI, Sisto NP, Ramirez AI, Magana RV (2019) Building urban resilience and knowledge co-production in the face of weather hazards: flash floods in the Monterrey Metropolitan Area (Mexico). Environ Sci Policy 99:37–47

    Article  Google Scholar 

  • Alfieri L, Bisselink B, Dottori F, Naumann G, De Roo A, Salamon P et al (2017) Global projections of river flood risk in a warmer world. Earth’s Fut 5(2):171–182

    Article  Google Scholar 

  • Ayyub BM (2014) Systems resilience for multihazard environments: definition, metrics, and valuation for decision making. Risk Anal 34(2):340–355

    Article  Google Scholar 

  • Beg I, Rashid T (2013) TOPSIS for hesitant fuzzy linguistic term sets. Int J Intell Syst 28(12):1162–1171

    Article  Google Scholar 

  • Bergstrand K, Mayer B, Brumback B, Zhang Y (2015) Assessing the relationship between social vulnerability and community resilience to hazards. Soc Indic Res 122(2):391–409

    Article  Google Scholar 

  • Bruneau M, Chang SE, Eguchi RT, Lee GC, O’Rourke TD, Reinhorn AM et al (2003) A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq Spectra 19(4):733–752

    Article  Google Scholar 

  • Chan SL, Wey WM, Chang PH (2014) Establishing disaster resilience indicators for tan-sui river basin in Taiwan. Soc Indic Res 115(1):387–418

    Article  Google Scholar 

  • Chang SE, Shinozuka M (2004) Measuring improvements in the disaster resilience of communities. Earthq Spectra 20(3):739–755

    Article  Google Scholar 

  • Cimellaro GP, Reinhorn AM, Bruneau M (2010) Framework for analytical quantification of disaster resilience. Eng Struct 32(11):3639–3649

    Article  Google Scholar 

  • Cutter SL, Barnes L, Berry M, Burton C, Evans E, Tate E et al (2008) A place-based model for understanding community resilience to natural disasters. Glob Environ Change 18(4):598–606

    Article  Google Scholar 

  • Cutter SL, Burton CG, Emrich CT (2010) Disaster resilience indicators for benchmarking baseline conditions. J Homel Secur Emerg Manag 7(1):51

    Google Scholar 

  • Cutter SL, Ash KD, Emrich CT (2014) The geographies of community disaster resilience. Glob Environ Change 29:65–77

    Article  Google Scholar 

  • Donegan HA, Dodd FJ (1991) A note on Saaty’s random indexes. Math Comput Model 15(10):135–137

    Article  Google Scholar 

  • Fang Q (2016) Adapting Chinese cities to climate change. Science 354(6311):425–426

    Article  Google Scholar 

  • Kahneman D, Tversky KA (1979) Prospect theory: an analysis of decision under risk. Econometrica 47(2):263–292

    Article  Google Scholar 

  • Keating A, Campbell K, Mechler R, Magnuszewski P, Mochizuki J, Liu W et al (2017) Disaster resilience: what it is and how it can engender a meaningful change in development policy. Dev Policy Rev 35(1):65–91

    Article  Google Scholar 

  • Liao H, Xu Z, Zeng XJ (2014) Distance and similarity measures for hesitant fuzzy linguistic term sets and their application in multi-criteria decision making. Inf Sci 271:125–142

    Article  Google Scholar 

  • Liao H, Wu D, Huang Y, Ren P, Xu Z, Verma M (2018) Green logistic provider selection with a hesitant fuzzy linguistic thermodynamic method integrating cumulative prospect theory and PROMETHEE. Sustainability 10:1291

    Article  Google Scholar 

  • Liu J, Diamond J (2005) China’s environment in a globalizing world. Nature 435(7046):1179–1186

    Article  Google Scholar 

  • Michel KE (2015) We must build resilience into our communities. Nature 524(7566):389

    Article  Google Scholar 

  • Min SK, Zhang X, Zwiers FW, Hegerl GC (2011) Human contribution to more-intense precipitation extremes. Nature 470(7334):378–381

    Article  Google Scholar 

  • Mishra AK, Coulibaly P (2009) Developments in hydrometric network design: a review. Rev Geophys 47(2):2001–2009

    Article  Google Scholar 

  • Norris FH, Stevens SP, Pfefferbaum B, Wyche KF, Pfefferbaum RL (2008) Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. Am J Commun Psychol 41(1–2):127–150

    Article  Google Scholar 

  • Peng JJ, Wang JQ, Wu XH (2016) Novel multi-criteria decision-making approaches based on hesitant fuzzy sets and prospect theory. Int J Inf Technol Decis Mak 15(03):621–643

    Article  Google Scholar 

  • Pfefferbaum RL, Pfefferbaum B, Horn RLV, Klomp RW, Reissman DB (2013) The communities advancing resilience toolkit (cart): an intervention to build community resilience to disasters. J Public Health Manag Pract 19(3):250–258

    Article  Google Scholar 

  • Ren P, Xu Z, Hao Z (2016) Hesitant fuzzy thermodynamic method for emergency decision making based on prospect theory. IEEE Trans Cybern 47(9):2531–2543

    Article  Google Scholar 

  • Rodriguez RM, Martinez L, Herrera F (2012) Hesitant fuzzy linguistic term sets for decision making. IEEE Trans Fuzzy Syst 20(1):109–119

    Article  Google Scholar 

  • Roxy MK, Ghosh S, Pathak A, Athulya R, Mujumdar M, Murtugudde R et al (2017) A threefold rise in widespread extreme rain events over central India. Nat Commun 8(1):1–11

    Article  Google Scholar 

  • Sherrieb K, Norris FH, Galea S (2010) Measuring capacities for community resilience. Soc Indic Res 99(2):227–247

    Article  Google Scholar 

  • Sherrieb K, Louis CA, Pfefferbaum RL, Pfefferbaum B, Diab E, Norris FH (2012) Assessing community resilience on the U.S. coast using school principals as key informants. Int J Disast Risk Reduct 2:6–15

    Article  Google Scholar 

  • Su Y, Sun XP, Zhao F (2017) Trust and its effects on the public’s perception of flood risk: a social science investigation of the middle and lower reaches of the Yangtze River. J Flood Risk Manag 10(4):487–498

    Article  Google Scholar 

  • Sun H, Cheng X, Dai M (2016) Regional flood disaster resilience evaluation based on analytic network process: a case study of the Chaohu Lake Basin, Anhui Province, China. Nat Hazards 82(1):39–58

    Article  Google Scholar 

  • Torra V (2010) Hesitant fuzzy sets. Int J Intell Syst 25(6):529–539

    Google Scholar 

  • Tüysüz F, Şimşek B (2017) A hesitant fuzzy linguistic term sets-based AHP approach for analyzing the performance evaluation factors: an application to cargo sector. Complex Intell Syst 3(3):167–175

    Article  Google Scholar 

  • Tversky KA, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertainty 5(4):297–323

    Article  Google Scholar 

  • United Nations (2017) The new urban agenda. http://habitat3.org/the-new-urban-agenda/

  • United Nations International Strategy for Disaster Reduction (2009) UNISDR terminology on disaster risk reduction. https://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf

  • World Meteorological Organization (2008) Guide to hydrological practices, 6th edn, WMO-No. 168, Geneva, Switzerland

  • Yin J, Gentine P, Zhou S, Sullivan SC, Wang R, Zhang Y, Guo S (2018) Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat Commun 9(1):4389

    Article  Google Scholar 

  • Yoon DK, Kang JE, Brody SD (2016) A measurement of community disaster resilience in Korea. J Environ Plan Manag 59(3):1–25

    Article  Google Scholar 

  • Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning. Inf Sci 8(3):199–249

    Article  Google Scholar 

  • Zhang X, Xu Z (2014) The TODIM analysis approach based on novel measured functions under hesitant fuzzy environment. Knowl-Based Syst 61:48–58

    Article  Google Scholar 

Download references

Acknowledgements

We are very grateful for all the support of National Natural Science Foundation of China (Grant Nos. 71771157, 71301109), Soft Science Program of Sichuan Province (Grant No. 2019JDR0129), Funding of Sichuan University (Grant No. skqx201726) and China Postdoctoral Science Foundation Funded Project (Grant No. 2017M610609).

Author information

Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610065, China

    Ying Luo & Liming Yao

  2. College of Management Science, Chengdu University of Technology, Chengdu, 610059, China

    Xudong Chen

  3. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, 610065, China

    Liming Yao

Authors
  1. Ying Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xudong Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liming Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xudong Chen.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

Relative importance score

Nature

Society

Economy

Infrastructure

Management

Nature

1

1

3

8

8

Society

 

1

3

5

7

Economy

  

1

5

8

Infrastructure

   

1

1

Management

    

1

Appendix 2

 

Precipitation during the flood season (mm)

Water surface rate (%)

Degree of terrain influence

Vegetation coverage (%)

Panel A. Natural dimension statistical data and raw linguistic information

Qinghai

56.87

1.70

Between higher and highest

5.63

Tibet

74.45

4.85

Between higher and highest

11.98

Sichuan

138.88

2.30

Between higher and highest

35.22

Yunnan

137.59

0.28

Between average and higher

50.03

Chongqing

157.75

2.99

Between average and higher

38.43

Hubei

154.49

1.40

Between lower and average

38.40

Hunan

154.21

6.39

Between average and higher

47.77

Jiangxi

179.85

10.00

Between lower and average

60.01

Anhui

132.54

8.10

Between lower and average

27.53

Jiangsu

159.05

16.80

Between lowest and lower

15.80

Shanghai

161.28

9.90

Between lowest and lower

10.74

 

Population density (p/km2)

Proportion of labor force (%)

Amount of medical staffs per unit (p/103p)

Amount of firefighters per unit (p/103p)

Panel B. Social dimension statistical data

Qinghai

8.58

72.06

6.98

0.33

Tibet

2.80

70.25

4.90

1.45

Sichuan

170.78

70.24

6.39

0.07

Yunnan

125.29

72.67

5.91

0.16

Chongqing

373.30

69.33

6.23

0.13

Hubei

318.39

71.95

6.77

0.10

Hunan

323.84

69.42

6.06

0.06

Jiangxi

276.87

68.64

5.10

0.13

Anhui

446.34

67.91

5.01

0.08

Jiangsu

753.19

72.62

6.82

0.08

Shanghai

2892.69

75.79

7.73

0.31

 

GDP density (106 yuan/km2)

Public budget income per unit of the government (yuan/p)

Net income per farmer (yuan/p)

Grain output per unit (kg/p)

Panel C. Economic dimension statistical data

Qinghai

0.38

4117.06

9462.3

171.40

Tibet

0.11

5514.24

10,330.2

316.02

Sichuan

7.61

4309.79

12,226.9

420.25

Yunnan

3.53

3928.70

9862.2

383.96

Chongqing

23.58

7324.81

12,637.9

351.19

Hubei

19.08

5487.03

13,812.1

480.76

Hunan

16.00

4020.15

12,935.8

448.05

Jiangxi

11.98

4861.66

13,241.8

480.68

Anhui

19.28

4496.32

12,758.2

642.64

Jiangsu

80.55

10,177.52

19,158.0

449.72

Shanghai

366.47

27,470.06

27,825.0

41.27

 

Dike length (km)

Reservoir capacity (108 m3)

Area of flood control zones (104 km2)

Density of hydrological network (km2/station)

Panel D. Infrastructural dimension statistical data

Qinghai

657.33

319.98

0.01

6019.17

Tibet

2023.57

34.16

0.01

14,119.54

Sichuan

5745

476.19

0.84

690.34

Yunnan

7847.81

795.94

0.21

382.62

Chongqing

1322.52

121.41

0.19

95.70

Hubei

26,284.66

1263.20

4.08

292.30

Hunan

18,684.87

513.67

2.20

375.53

Jiangxi

13,028.89

319.88

2.11

140.02

Anhui

34,463.3

325.09

1.89

178.47

Jiangsu

55,331.54

35.36

1.82

276.29

Shanghai

6812.07

5.49

0.60

352.25

 

Disaster prevention and control plan

Rescue ability

Resource allocation ability

Panel E. Management dimension raw linguistic information

Qinghai

Between lower and average

Between lower and average

Between average and higher

Tibet

Between lower and average

Between lowest and lower

Between lowest and lower

Sichuan

Between higher and highest

Between higher and highest

Between average and higher

Yunnan

Between average and higher

Between average and higher

Between average and higher

Chongqing

Between average and higher

Between average and higher

Between average and higher

Hubei

Between higher and highest

Between higher and highest

Between average and higher

Hunan

Between higher and highest

Between average and higher

Between average and higher

Jiangxi

Between average and higher

Between average and higher

Between average and higher

Anhui

Between average and higher

Between average and higher

Between average and higher

Jiangsu

Between higher and highest

Between higher and highest

Between higher and highest

Shanghai

Between higher and highest

Between higher and highest

Between higher and highest

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, Y., Chen, X. & Yao, L. Flood disaster resilience evaluation of Chinese regions: integrating the hesitant fuzzy linguistic term sets with prospect theory. Nat Hazards 105, 667–690 (2021). https://doi.org/10.1007/s11069-020-04330-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-020-04330-z

Keywords

Search

Navigation