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Water Resources Management

, Volume 30, Issue 9, pp 3027–3042 | Cite as

Water Resources Risk Assessment Model based on the Subjective and Objective Combination Weighting Methods

  • Jun Zhao
  • Juliang Jin
  • Jiezhong Zhu
  • Jinchao Xu
  • Qingfeng Hang
  • Yaqian Chen
  • Donghao Han
Article

Abstract

In order to fully protect water resources and govern water pollution, a water resources risk assessment model based on the subjective and objective combination weighting methods is studied in this paper. The model takes risk indices in water resources system as research object, and extracts the different index information from various angles. In this study, firstly, according to system evaluation standard, it constructs the judgment matrix by the subjective judgment and objective calculation method. Secondly, it applies the improved analytic hierarchy process with accelerating genetic algorithm for modifying the consistency of judge matrix to calculate the single sample weight and sample set weight. Thirdly, it uses water resources type of evaluation index and the level of sample evaluation index values to determine the combination weights. After that, the systematic comprehensive evaluation index can be obtained by taking a weighted Average of these combined weights and the consistent-dimensionless sample values for each evaluation index. Then, it classifies and sorts the samples by the comprehensive evaluation value. Finally, on the basis of risk level, it puts forward the suitable planning for water resources development with reference to the real condition. This study takes Hanjiang Basin for example. After risk assessment model establishment, validation and application between the actual and simulated data, the results show that the trend of water resources risk is considered to be controlled at a certain level. This is matched with the fact. The model is feasible in scientific method, and reasonable in conclusion, which can be applied to water resources risk assessment research.

Keywords

Water resource Risk assessment Improved analytic hierarchy process Accelerating genetic algorithm The combination weighting methods 

Notes

Acknowledgments

The authors appreciate the support of the National Natural Science Foundation of China (Grant No. 51409141, No. 51579059, No. 51479045, No. 51309004), the Natural Science Research Program for University of Jiangsu Province, China (Grant No.14KJD570001), Specific Funds for Basic Scientific Research of Central Public Scientific Institutes (Grant No. Y115001), Research Funding for Postdoctoral Program of Jiangsu Province, China (Grant No.1302113C), Postdoctoral Science Foundation of Nanjing Hydraulic Research Institute (Grant No. BH11301), and Open Research Foundation for Huai River Meteorology of China (Grant No. HRM201503). The authors also want to thank the people for their helpful suggestions and corrections on the earlier draft of our study according to which we improved the content.

References

  1. Benedini M, Tsakiris G (2013) Water quality in the context of water resources management. SpringerGoogle Scholar
  2. Carrasco FM, Garrote L, Iglesias A, Mediero L (2013) Diagnosing causes of water scarcity in complex water resources systems and identifying risk management actions. Water Resour Manag 27(6):1693–1705CrossRefGoogle Scholar
  3. Chen S (1990) Fuzzy hydrology and water resources system fuzzy optimization theory. Dalian University of Technology Press, Beijing (In Chinese)Google Scholar
  4. Chen S (2002) System optimizing and fuzzy identification theory and application in the complex water resources. Jilin University Press, Changchun (In Chinese)Google Scholar
  5. Chen S, Hu J (2006) Variable fuzzy assessment method and its application in assessing water resources carrying capacity. Water Resour Prot 37(3):264–277Google Scholar
  6. Christodoulou SE (2011) Water resources conservancy and risk reduction under climatic instability. Water Resour Manag 25(4):1059–1062CrossRefGoogle Scholar
  7. Ding Y, Liang X, Cheng L, Wang W, Li R (2012) An integrated intelligent cooperative model for water-related risk management and resource scheduling. Handb Decis Mak 33:373–402CrossRefGoogle Scholar
  8. Fang H (2004) Water regulation theory in regional water resources rational allocation. Yellow River Water Conservancy Press, Zhengzhou (In Chinese)Google Scholar
  9. Gu W, Shao D, Huang X, Dai T (2009) Multi-objective risk assessment on water resources optimal allocation. Adv Water Resour Hydraul Eng 361–366Google Scholar
  10. Hlavinek P, Popovska C, Marsalek J, Mahrikova I, Kukharchyk T (2009) Risk management of water supply and sanitation systems. SpringerGoogle Scholar
  11. Hu M, Guo Z (1998) Evaluation of lake eutrophication by means of fuzzy neural network. Res Environ Sci 11(4):40–42 (In Chinese)Google Scholar
  12. Jin J, Huang H, Wei Y (2004) Comprehensive evaluation model for water quality based on combined weights. J Hydroelectric Power 23(3):13–19 (In Chinese)Google Scholar
  13. Jin JL, Wu K, Li J (2007) Entropy coupling method of evaluating Chaohu lake water quality security using correspondence factor analysis and projection pursuit. J Sichuan Univ (Eng Sci Ed) 39:7–13 (In Chinese)Google Scholar
  14. Kaviani S, Hassanli AM, Homayounfar M (2015) Optimal crop water allocation based on constraint-state method and nonnormal stochastic variable. Water Resour Manag 29(4):1003–1018CrossRefGoogle Scholar
  15. Li H, Wang P (1994) Fuzzy mathematics. National Defence Industry Press, Beijing (In Chinese)Google Scholar
  16. Li Z, Ding J, Peng L (2004) Environment quality evaluation principles and methods. Chemical Industry Press, Beijing (In Chinese)Google Scholar
  17. Li R, Hong T, Jin J (2007) Research on fuzzy risk assessment model for river water quality. J Wuhan Univ Technol 29:43–46, (In Chinese)Google Scholar
  18. Li P, Qian H, Wu J, Chen J (2013a) Sensitivity analysis of TOPSIS method in water quality assessment: I. Sensitivity to the parameter weights. Environ Monit Assess 185(3):2453–2461CrossRefGoogle Scholar
  19. Li P, Qian H, Wu J, Chen J (2013b) Sensitivity analysis of TOPSIS method in water quality assessment II: sensitivity to the index input data. Environ Monit Assess 185(3):2463–2474CrossRefGoogle Scholar
  20. Lin H (2003) Water resource management theory and application. China WaterPower Press, Beijing (In Chinese)Google Scholar
  21. Litvinov AS, Zakonnova AV, Sokolova EN (2010) Hydrological structure of the Sheksna river deep of the Rybinsk reservoir and water quality assessment by biological parameters. Russ Meteorol Hydrol 35(1):62–67. doi: 10.3103/S1068373910010097 CrossRefGoogle Scholar
  22. Liu W, Lei Z (1997) Study on rectification method for the judgment matrix in AHP. Syst Eng Theory Pract 17(6):30–34, 39Google Scholar
  23. Luo J, Xie J, Ruan B (2008) Fuzzy comprehensive assessment model for water shortage risk based on entropy weight. J Hydraul Eng 39(9):1092–1097 (In Chinese)Google Scholar
  24. Marton D, Kapelan Z (2014) Risk and reliability analysis of open reservoir water shortages using optimization. Procedia Eng 89:1478–1485CrossRefGoogle Scholar
  25. Persson K, Destounia G (2009) Propagation of water pollution uncertainty and risk from the subsurface to the surface water system of a catchment. J Hydrol 377(3–4):434–444CrossRefGoogle Scholar
  26. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkGoogle Scholar
  27. Shao L, Luan S (2010) A CVaR-based nonlinear stochastic model for water resources management. Conference on Environmental Pollution and Public Health, Wuhan, pp 1383–1386Google Scholar
  28. Siew C, Tanyimboh TT, Seyoum AG (2014) Assessment of penalty-free multi-objective evolutionary optimization approach for the design and rehabilitation of water distribution systems. Water Resour Manag 28(2):373–389. doi: 10.1007/s11269-013-0488-8 CrossRefGoogle Scholar
  29. Song B, Pan G, Hu Y, Xu D (2001) Fuzzy AHP of torpedo system based on entropy weight. Syst Eng Theory Pract 21(4):129–132, 136 (In Chinese)Google Scholar
  30. Spiller M, McIntosh BS, Seaton RAF, Jeffrey PJ (2015) Integrating process and factor understanding of environmental innovation by water utilities. Water Resour Manag 29(6):1979–1993CrossRefGoogle Scholar
  31. Sun C, Wang J, Pan J (2001) Research on the analysis method for assessing the degree sustainable development and utilization of water resources in agricultural and stock raising base. Syst Sci Compr Stud Agric 17(2):81–83 (In Chinese)Google Scholar
  32. Thissen W, Kwakkel J, Mens M, van der Sluijs J, Stemberger S, Wardekker A, Wildschut D (2015) Dealing with uncertainties in fresh water supply: experiences in the Netherlands. Water Resour Manag 1–23Google Scholar
  33. Wang H, Qian L, Xu X, Wang Y (2009) Model for evaluating water shortage risk based on fuzzy probability and its application. J Hydraul Eng 40(7):813–821Google Scholar
  34. Wang H, Qiu Y, Jia Y (2010a) Development course and tendency of water resources assessment. J Beijing Normal Univ (Nat Sci) 46(3):274–277 (In Chinese)Google Scholar
  35. Wang J, Cai S, Yang Y, Li R (2010b) Study on comprehensive grade evaluation of river health to Yihe river based on projection pursuit of entropy. J Water Resour Water Eng 21(3):120–123Google Scholar
  36. Wu P, Tan M (2012) Challenges for sustainable urbanization: a case study of water shortage and water environment changes in Shandong, China. Procedia Environ Sci 13:919–927CrossRefGoogle Scholar
  37. Zhang J, Lin X, Guo B (2016) Multivariate copula-based joint probability distribution of water supply and demand in irrigation district. Water Resour Manag 1–15Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jun Zhao
    • 1
  • Juliang Jin
    • 2
  • Jiezhong Zhu
    • 3
  • Jinchao Xu
    • 4
  • Qingfeng Hang
    • 5
  • Yaqian Chen
    • 6
  • Donghao Han
    • 1
  1. 1.College of HydrometeorologyNanjing University of Information Science and TechnologyNanjingChina
  2. 2.School of Civil EngineeringHefei University of TechnologyHefeiChina
  3. 3.Binjiang CollegeNanjing University of Information Science and TechnologyNanjingChina
  4. 4.Hydraulic Engineering DepartmentNanjing Hydraulic Research InstituteNanjingChina
  5. 5.Yancheng Branch OfficeJiangsu Provincial Hydrology and Water Resources Investigation BureauYanchengChina
  6. 6.Pearl Water Resources Protection InstituteGuangzhouChina

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