Skip to main content
SpringerLink
Log in

Fast Comprehensive Flood Risk Assessment Based on Game Theory and Cloud Model Under Parallel Computation (P-GT-CM)

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

Flood risk assessment is the fundamental work of flood risk management and important decision-making basis for essential flood mitigation, and it is an attractive and difficult problem with more requirements on convenience, effectiveness and timeliness. Specifically, the uncertainty and nonlinear relation between assessment indices and evaluation levels are always difficult to be revealed, and it is not easy to calculate the weight of assessment indices by subjective judgment and objective properties. Moreover, reducing the total computational time for rapid flood risk map application is rarely studied. On the basis of cloud model (CM), game theory (GT) and parallel computation technology (PC), a new model named P-CM-GT for fast comprehensive flood risk assessment was presented, which has three advantages, i.e. firstly, it could describe the fuzziness randomness of membership degree via CM; secondly, the combination weight integrating with different weights is employed via GC; thirdly, the computation process of CM and GT is combined with PC to reduce the running time. Finally, taking a case study on fast comprehensive flood risk assessment of Hubei Province in China, the flood risk grades were achieved with less time, and the results were appropriately consistent with the actual situation, and the future flood control focus is to set up a wholesome and effective emergency plan. Moreover, the proposed model is feasible, effective, fast and applicable, thus give out a novel thinking for fast flood risk management.

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
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Chen SY, Guo Y (2006) Variable fuzzy sets and its application in comprehensive risk evaluation for flood-control engineering system. Fuzzy Optim Decis Making 5:153–162

    Article  Google Scholar 

  • Chen L, Huang YC, Bai RZ et al (2017) Regional disaster risk evaluation of China based on the universal risk model. Nat Hazards 89:647–660

    Article  Google Scholar 

  • Cheng K, Fu Q, Meng J, Li TX, Pei W (2018) Analysis of the spatial variation and identification of factors affecting the water resources carrying capacity based on the cloud model. Water Resour Manag 32:2767–2781. https://doi.org/10.1007/s11269-018-1957-x

    Article  Google Scholar 

  • Deng JL (1989) Introduction to grey system theory. J Grey Syst 1:1–24

    Google Scholar 

  • Du J, He F, Shi PJ (2006) Integrated flood risk assessment of Xiangjiang River basin in China. J Nat Disaster 15(6):38–44 (in Chinese)

    Google Scholar 

  • Feng ZK, Niu WJ, Zhou JZ et al (2017) Scheduling of short-term hydrothermal energy system by parallel multi-objective differential evolution. Appl Soft Comput 61:58–71

    Article  Google Scholar 

  • Hajkowicz S, Collins K (2007) A review of multiple criteria analysis for water resource planning and management. Water Resour Manag 21:1553–1566. https://doi.org/10.1007/s11269-006-9112-5

    Article  Google Scholar 

  • Huang LM, Liu CW (2006) The features and reasons of the flood disaster in Hubei Province in modern times. J Central China Normal Univ 40(1):115–118 (in Chinese)

    Google Scholar 

  • Jean HD, Samuel NO, Bachir MS et al (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques. Geoenviron Disasters 3(10):1–13

    Google Scholar 

  • Ji ZH, Li N, Xie W et al (2013) Comprehensive assessment of flood risk using the classification and regression tree method. Stoch Env Res Risk A 27(8):1815–1828

    Article  Google Scholar 

  • Kellens W, Vanneuville W, Verfaillie E, Meire E, Deckers P, Maeyer P (2013) Flood risk management in flanders: past developments and future challenges. Water Resour Manag 27:3585–3606. https://doi.org/10.1007/s11269-013-0366-4

    Article  Google Scholar 

  • Lai CG, Chen XH, Chen XY et al (2015) A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory. Nat Hazards 77:1243–1259

    Article  Google Scholar 

  • Li DY, Meng H, Shi X (1995) Membership clouds and membership cloud generators. Comput Res Dev 32:15–20 (in Chinese)

    Google Scholar 

  • Li M, Zheng TY, Zhang J, Fang Y, Liu J, Zheng X, Peng H (2019) A new risk assessment system based on set pair analysis – variable fuzzy sets for underground reservoirs. Water Resour Manag 33:2507–2522. https://doi.org/10.1007/s11269-019-02390-w

    Article  Google Scholar 

  • Liao L, Zhou JZ, Zou Q (2013) Weighted fuzzy kernel-clustering algorithm with adaptive differential evolution and its application on flood classification. Nat Hazards 63(1):279–293

    Article  Google Scholar 

  • Liu J, Wang SY, Li DM (2014) The analysis of the impact of land-use changes on flood exposure of Wuhan in Yangtze River basin, China. Water Resour Manag 28:4997–5014. https://doi.org/10.1007/s11269-014-0623-1

    Article  Google Scholar 

  • Liu D, Gong FH, Fu Q et al (2017) Evaluation model of irrigation water use efficiency based on game theory and GRA-TOPSIS. Trans Chin Soc Agric Mach 48(5):218–226 (in Chinese)

  • Lu HW, Ren LX, Chen YZ et al (2017) A cloud model based multi-attribute decision making approach for selection and evaluation of groundwater management schemes. J Hydrol 555:881–893

    Article  Google Scholar 

  • Mao DF, Duan ML, Li XZ et al (2013) Selection of deep water floating oil platform based on grey correlation. Pet Explor Dev 40(6):796–800

    Article  Google Scholar 

  • Roger BM (1991) Game theory: analysis of conflict. Harvard University Press, Cambridge

    Google Scholar 

  • Söderholm K, Pihlajamäki M, Dubrovin T, Veijalainen N, Vehviläinen B, Marttunen M (2018) Collaborative planning in adaptive flood risk management under climate change. Water Resour Manag 32:1383–1397. https://doi.org/10.1007/s11269-017-1875-3

    Article  Google Scholar 

  • Sun P, Jiang ZQ, Wang TT, Zhang YK (2016) Research and application of parallel normal cloud mutation shuffled frog leaping algorithm in cascade reservoirs optimal operation. Water Resour Manag 30:1019–1035. https://doi.org/10.1007/s11269-015-1208-3

    Article  Google Scholar 

  • Tang Z, Yi S, Wang C et al (2018) Incorporating probabilistic approach into local multi-criteria decision analysis for flood susceptibility assessment. Stoch Env Res Risk A 32(3):1–14

    Article  Google Scholar 

  • Tesfamariam S, Sadiq R (2008) Probabilistic risk analysis using ordered weighted averaging (OWA) operators. Stoch Env Res Risk A 22:1–15

    Article  Google Scholar 

  • Wang XJ, Zhao RH, Hao YW (2011a) Flood control operations based on the theory of variable fuzzy sets. Water Resour Manag 25:777–792. https://doi.org/10.1007/s11269-010-9726-5

    Article  Google Scholar 

  • Wang YM, Li ZW, Tang ZH et al (2011b) A GIS-based spatial multi-criteria approach for flood risk assessment in the Dongting Lake region, Hunan, Central China. Water Resour Manag 25:3465–3484. https://doi.org/10.1007/s11269-011-9866-2

    Article  Google Scholar 

  • Wang ZL, Lai CG, Chen XH et al (2015) Flood hazard risk assessment model based on random forest. J Hydrol 527(2015):1130–1141

    Article  Google Scholar 

  • Wang J, Peng L, Li N et al (2017) Risk assessment of the urban rail transit PPP projects based on OWA-ER. J Civ Eng Manag 34(5):46–51

    Google Scholar 

  • Wu TY, Lee WT, Nadra G et al (2014) Incentive mechanism for P2P file sharing based on social network and game theory. J Netw Comput Appl 41:47–55

    Article  Google Scholar 

  • Wu YN, Zhong PA, Xu B et al (2017) Changing of flood risk due to climate and development in Huaihe River basin, China. Stoch Environ Res Risk Assess 31(4):935–948. https://doi.org/10.1007/s00477-016-1262-2

    Article  Google Scholar 

  • Xu SJ (2017) Reflections on the flood prevention in Hubei Province in 2016. China Flood Drought Manag 27(1):52–57 (in Chinese)

    Google Scholar 

  • Yager RR (1998) On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  Google Scholar 

  • Yang X, Ding J, Hou H (2013) Application of a triangular fuzzy AHP approach for flood risk evaluation and response measures analysis. Nat Hazards 68(2):657–674

    Article  Google Scholar 

  • Yang WZ, Zhang BW, Fu RM (2017) A review of flood disasters in 2016 of China. China Flood Drought Manag 27(1):26–29

    Google Scholar 

  • Yang SM, Han XQ, Cao B et al (2018) Cloud-model-based method for risk assessment of mountain torrent disasters. Water 10:830

    Article  Google Scholar 

  • Zhao J, Jin JL, Guo QZ et al (2014) Dynamic risk assessment model for flood disaster on a projection pursuit cluster and its application. Stoch Env Res Risk A 28(8):2175–2183

    Article  Google Scholar 

  • Zou Q, Zhou JZ, Zhou C et al (2012) The practical research on flood risk analysis based on IIOSM and fuzzy α-cut technique. Appl Math Model 36:3271–3282. https://doi.org/10.1016/j.apm.2011.10.008

    Article  Google Scholar 

  • Zou Q, Zhou JZ, Zhou C et al (2013) Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch Env Res Risk A 27(2):525–546. https://doi.org/10.1007/s00477-012-0598-5

    Article  Google Scholar 

Download references

Acknowledgements

This work is financially supported by the National Key R&D Program of China (Item Nos. 2016YFC0402202), the Open Research Program of Changjiang River Scientific Research Institute (Grant No. CKWV2017505/KY) and the National Natural Science Foundation of China (Grant No. 91647114 and 51509009).

Author information

Authors and Affiliations

  1. Changjiang Institute of Survey, Planning, Design and Research, Wuhan, 430010, China

    Qiang Zou

  2. College of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, People’s Republic of China

    Li Liao

  3. Department of Biological and Agricultural Engineering & Zachry Department of Civil Engineering, Texas A&M University, College Station, TX, 77843-2117, USA

    Li Liao

  4. College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Hui Qin

Authors
  1. Qiang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qiang Zou.

Ethics declarations

Conflict of Interest

None.

Additional information

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zou, Q., Liao, L. & Qin, H. Fast Comprehensive Flood Risk Assessment Based on Game Theory and Cloud Model Under Parallel Computation (P-GT-CM). Water Resour Manage 34, 1625–1648 (2020). https://doi.org/10.1007/s11269-020-02495-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-020-02495-7

Keywords

Search

Navigation