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


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.


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



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.


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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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