Skip to main content
Log in

Water Environment Random Evaluation Model based on the improved TOPSIS method and Bayesian Theory and its Application

  • WATER RESOURCES AND THE REGIME OF WATER BODIES
  • Published:
Water Resources Aims and scope Submit manuscript

Abstract

Under the background analysis of water issues, water environment random evaluation model based on Bayesian theory is put forward to universally describe and physically analyze the uncertainty information. Guided by the viewpoint of sustainable development, this study applies water conservancy science, intelligence science and information science to discuss about risk indexes from three aspects of water quantity, water quality, and water ecology with the evolution mechanism of water environment. The evaluation index system is selected by qualitative analysis and quantitative calculation, and index weight is determined by the improved TOPSISI method. The Bayesian theory is employed to set up the random evaluation model. The process is to obtain posterior distribution by prior distribution with sample information. Then, the evaluation levels of water environment are given by the principle of probability maximization with advancing the control policy. Taihu Basin, China is taken as an example. It shows that the proposed model is rigorous with theory, flexible with method, and reasonable with results, providing a new way for studying water resources shortage, water pollution prevention, and water ecology protection, which can be widely applied to water environment management.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

REFERENCES

  1. Andrew, A.A., Gloria, E.T.E., and George, E.N., Water resources management and integrated water resources management (IWRM) in Cameroon, Water Resour. Manag., 2010, vol. 24, pp. 871–888.

    Article  Google Scholar 

  2. Benedini, M. and Tsakiris, G., Water Quality in the Context of Water Resources Management, Springer, 2013.

    Book  Google Scholar 

  3. Carrasco, F.M., Garrote, L., Iglesias, A., and Mediero, L., Diagnosing causes of water scarcity in complex water resources systems and identifying risk management actions, Water Resour. Manag., 2013, vol. 27, pp. 1693–1705.

    Article  Google Scholar 

  4. Chen, S.Y., Water Resources and Flood Control System of Variable Fuzzy Sets Theory and Method, Dalian Univ. Technol. Press, 2005.

  5. Chen, X.H., Jiang, T., and Chen, J.H., The Water Environment Evaluation and Planning, China Water Conservancy and Hydropower Press, 2007.

    Google Scholar 

  6. Christodoulou, S.E., Water resources conservancy and risk reduction under climatic instability, Water Resour. Manag., 2011, vol. 25, pp. 1059–1062.

    Article  Google Scholar 

  7. Cobbina, S.J., Anyidoho, L.Y., Nyame, F., and Hodgson, I.O.A., Water quality status of dugouts from five districts in Northern Ghana: implications for sustainable water resources management in a water stressed tropical savannah environment, Environ. Monit. Assess., 2010, vol. 167, pp. 405–416.

    Article  Google Scholar 

  8. Ding, J., Collection of Hydrology and Water Resources, Sichuan Science and Technology Press, 2006.

    Google Scholar 

  9. Ding, Y.S., Liang, X., Cheng, L.J., Wang, W., and Li, R.F., An integrated intelligent cooperative model for water-related risk management and resource scheduling, Handbook on Decision Making, 2012, vol. 33, pp. 373–402.

    Article  Google Scholar 

  10. Dong, Z.C., Water Resources System Analysis, China Water Conservancy and Hydropower Press, 2008.

    Google Scholar 

  11. Freni, G., Mannina, G., and Viviani, G., Assessment of the integrated urban water quality model complexity through identifiability analysis, Water Res., 2010, pp. 1–14.

  12. Gu, W.Q., Shao, D.G., Huang, X.F., and Dai, T., Multi-objective risk assessment on water resources optimal allocation, Advances in Water Resources and Hydraulic Engineering, 2009, pp. 361–366.

    Book  Google Scholar 

  13. Guana, X.J., Liub, W.K., and Chenb, M., Study on the ecological compensation standard for river basin water environment based on total pollutants control, Ecol. Indic., 2016, vol. 69, pp. 446–452.

    Article  Google Scholar 

  14. Hao, J.F., The Characteristics of Multi-Functional Landscape and the Response of the Water Environment of the Wetland in Ubanization Area–A Case Study of the New City of Xianlin, Nanjing, China, Nanjing Normal Univ., 2012.

    Google Scholar 

  15. Hlavinek, P., Popovska, C., Marsalek, J., Mahrikova, I., and Kukharchyk, T., Risk Management of Water Supply and Sanitation Systems, Springer, 2009.

  16. Jin, J.L., Wu, K.Y., and Li, J.Q., Entropy coupling method of evaluating Chaohu Lake water quality security using correspondence factor analysis and projection pursuit, J. Sichuan Univ. (Engineering Sci.), 2007, vol. 39, pp. 7–13.

    Google Scholar 

  17. Kılkış, Ş., Sustainable development of energy, water and environment systems index for Southeast European cities, J. Clean. Prod., 2016, vol. 130, pp. 222–234.

    Article  Google Scholar 

  18. Koundouri, P. and Papandreou, N.A., Water Resources Management Sustaining Socio-Economic Welfare, Springer, 2014.

    Book  Google Scholar 

  19. Li, P.Y., Qian, H., Wu, J.H., and Chen, J., Sensitivity analysis of TOPSIS method in water quality assessment I: Sensitivity to the parameter weights, Environ. Monit. Assess., 2013, vol. 185, pp. 2453–2461.

    Article  Google Scholar 

  20. Li, P.Y., Qian, H., Wu, J.H., and Chen, J., Sensitivity analysis of TOPSIS method in water quality assessment II: Sensitivity to the index input data, Environ. Monit. Assess., 2013, vol. 185, pp. 2463–2474.

    Article  Google Scholar 

  21. Liang, Z.M., Dai, R., and Li, B.Q., A review of hydrological uncertainty analysis based on the Bayesian theory, Adv. Water Sci., 2010, vol. 21, pp. 274–281.

    Google Scholar 

  22. Litvinov, A.S., Zakonnova, A.V., and Sokolova, E.N., Hydrological structure of the Sheksna River Deep of the Rybinsk Reservoir and water quality assessment by biological parameters, Russ. Meteorol. Hydrol., 2010, vol. 35, pp. 62–67.

    Article  Google Scholar 

  23. Liu, J.L., Chen, Q.Y., and Li, Y.L., Ecological risk assessment of water environment for Luanhe River Basin based on relative risk model, Ecotoxicol., 2010, vol. 19, pp. 1400–1415.

    Article  Google Scholar 

  24. Lu, S.B., Bao, H.J., and Pan, H.L., Urban water security evaluation based on similarity measure model of Vague sets, Int. J. Hydrogen Energy, 2016, vol. 41, pp. 15944–15950.

    Article  Google Scholar 

  25. Persson, K. and Destounia, G., Propagation of water pollution uncertainty and risk from the subsurface to the surface water system of a catchment, J. Hydrol., 2009, vol. 377, pp. 434–444.

    Article  Google Scholar 

  26. Shao, L.G. and Luan, S.G., A CVaR-Based Nonlinear Stochastic Model for water resources management, Conference on Environmental Pollution and Public Health, Wuhan, 2010, pp. 1383–1386.

  27. Shi, Y., Liang, Z.M., and Yi, Z.Z., The random assessment model of regional comprehensive drought and its application, Water Resour. Power, 2011, vol. 29, no. 9, pp. 1–3.

    Google Scholar 

  28. Wang, H., Chou, Y., and Jia, Y., Development course and tendency of water resources assessment, J. Beijing Normal Univ. (Natural Sci. Edition), 2010, vol. 46, pp. 274–277.

    Google Scholar 

  29. Wang, J.L., Assessment of Aquaitic Ecological Function Based on the Third Level Aquatic Ecoregion of Liaohe River Basin, Liaoning Univ., 2013.

    Google Scholar 

  30. Wang, M.W., Jin, J.L., and Zhou, Y.L., Set Pair Analysis Coupling Method and Application, Sci. Press, 2014.

    Google Scholar 

  31. Wang, S.C., The harmony between man and nature—China’s water problems and countermeasures, J. Beijing Normal Univ. (Natural Sci. Edition), 2009, vol. 45, pp. 441–445.

    Google Scholar 

  32. Wang, W.S., Jin, J.L., and Ding, J., Stochastic Hydrology, China WaterPower Press, 2016.

    Google Scholar 

  33. Wu, K.Y., Jin, J.L., and Wang, W.S., Combination evaluation model based on set pair analysis and its application, The Practice and Understanding of Mathematics, 2013, vol. 43, pp. 1–6.

    Google Scholar 

  34. Xia, J., Research on vulnerability assessment of river basin water resources and adaptation countermeasures under the background of climate change, taking the advantages of resources science and technology, ensuring western innovation and development, China Natural Resour. Inst. Acad. Annual Meeting 2011, Urumqi, 2011.

  35. Xiao, J.H., Shi, G.Q., and Mao, C.M., The three gorges project pre-evaluation of valuation effects of TGP on river ecosystem service, J. Natural Resour., 2006, vol. 21, pp. 424–431.

    Google Scholar 

  36. Xu, J.C., Xuan, G.X., Li, Y. Hu, Y.A., Li, Z.H., and Jin, Y., Study on the squat of extra-large scale ship in the Three Gorges Ship Lock, Ocean Eng., 2016, vol. 123, pp. 65–74.

    Article  Google Scholar 

  37. Yao, Z.M. and Zhang, J.Y., The research progress of water resources evaluation, Water Resour. Res., 2009, vol. 30, pp. 16–18.

    Google Scholar 

  38. Zhao, J., Huang, Z.P., Jin, J.L., Lu, B.H., Zhang, X.M., Chen, Y.Q., Risk assessment of regional water resources and forewarning model at different time scales, J. Hydrol. Eng., 2013, vol. 18, pp. 1114–1121.

    Article  Google Scholar 

  39. Zhao, J., Jin, J.L., Guo, Q.Z., Liu, L., and Yaqian, C., Dynamic risk assessment model for flood disaster on projection pursuit cluster and its application, Stoch. Env. Res. Risk A., 2014, vol. 28, pp. 2175–2183.

    Article  Google Scholar 

  40. Zhao, J., Jin, J.L., Guo, Q.Z., Chen, Y.Q., Lu, M.X., and Tinoco, L., Forewarning model for water pollution risk based on Bayes theory, Environ. Sci. Poll. R., 2014, vol. 21, pp. 3073–3081.

    Article  Google Scholar 

  41. Zhao, J., Jin, J.L., Zhang, X.M., and Chen, Y.Q., Risk dynamic evaluation model for basin water quality based on projection pursuit cluster principle, Hydrol. Res., 2012, vol. 43, pp. 798–807.

    Article  Google Scholar 

Download references

FUNDING

The authors gratefully appreciate the financial support of China Scholarship Council (Grant nos. 201 808 320 127, 201 808 320 128), the support of National Natural Science Foundation of China (Grant nos. 51 409 141, 51 579 059, 51 479 045, 51 309 004), the Open Research Foundation for Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology (Grant no. JKLAM1701), and Nanjing University of Information Science and Technology Research Foundation (Grant no. 2017r097). 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.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jinchao Xu, Yaqian Chen, Jun Zhao, Qingfeng Hang or Xuechun Li.

Additional information

The article is published in the original.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jinchao Xu, Chen, Y., Zhao, J. et al. Water Environment Random Evaluation Model based on the improved TOPSIS method and Bayesian Theory and its Application. Water Resour 46, 344–352 (2019). https://doi.org/10.1134/S0097807819030102

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0097807819030102

Keywords:

Navigation