Abstract
In order to reduce the losses by water pollution, forewarning model for water pollution risk based on Bayes theory was studied. This model is built upon risk indexes in complex systems, proceeding from the whole structure and its components. In this study, the principal components analysis is used to screen out index systems. Hydrological model is employed to simulate index value according to the prediction principle. Bayes theory is adopted to obtain posterior distribution by prior distribution with sample information which can make samples’ features preferably reflect and represent the totals to some extent. Forewarning level is judged on the maximum probability rule, and then local conditions for proposing management strategies that will have the effect of transforming heavy warnings to a lesser degree. This study takes Taihu Basin as an example. After forewarning model application and vertification for water pollution risk from 2000 to 2009 between the actual and simulated data, forewarning level in 2010 is given as a severe warning, which is well coincide with logistic curve. It is shown that the model is rigorous in theory with flexible method, reasonable in result with simple structure, and it has strong logic superiority and regional adaptability, providing a new way for warning water pollution risk.
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Acknowledgments
The authors would like to thank the support of the Major Special Technological Program of Water Pollution Control and Management (Program No.2009ZX07106-001), the Public Welfare Industry Funding for Research and Special Projects of Ministry of Water Resources of China (No. 201301003), the National Natural Science Funds of China (No. 51079037, No. 51309072 and No. 51309004), Nanjing University of Information Science & Technology Research Foundation (S8112077001), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). The authors also want to thank the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper according to which we improved the content.
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This paper is a proposed model as a tool for forewarning water pollution risk.
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Zhao, J., Jin, J., Guo, Q. et al. Forewarning model for water pollution risk based on Bayes theory. Environ Sci Pollut Res 21, 3073–3081 (2014). https://doi.org/10.1007/s11356-013-2222-8
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DOI: https://doi.org/10.1007/s11356-013-2222-8