Water Quality Evaluation And Correlation Analysis Of Indicators Based On Bayesian Network

With the development of social economy,the pollutants discharged into rivers are increasing,the quality of water environment in river basins is declining and water quality assessment is increasingly concerned by the society as a basic step in water environment management and water pollution prevention.Because the water quality assessment is a comprehensive evaluation process involving multiple indicators,it has a certain nonlinear correlation.And the existing surface water quality assessment method cannot represent the quantitative relationship between indicators.Based on Bayesian network has strong causal reasoning ability,intuitive display of correlation between variables,and better handling of uncertainty problems.In view of the complexity of water quality information and the uncertainty of pollution changes in water environment system,this study introduces Bayesian network theory into water quality.In the evaluation,a new method of water quality evaluation based on Bayesian network is established through the learning,construction and reasoning of Bayesian network,and the correlation between water quality factors is analyzed.Taking the water quality data of Hongya section of Qingyi River from 2013 to 2015 as sample data,Select six water quality variables and grade F variables of DO,COD_Mn,COD_Cr,BOD₅,NH₃-N,and TN.The network structure learning algorithm combining K2 search and mutual information,the network parameter learning method of maximum likelihood estimation,and the joint tree inference algorithm of two-way reasoning are used to establish the Bayesian network model for water quality evaluation and the following conclusions are obtained:1)The water quality of the 12-month section of the Muchengzhen in 2015 is evaluated,and the correct rate of the model reached 83.3%,which proved the correctness.2)The correlation analysis of water quality factors.The direct pollution indicators of the river section are TN,NH₃-N,COD_Mn,in addition.The change of TN index concentration will have a direct impact on other indicators.The change of NH₃-N index concentration will have a direct impact on COD_Mn index,which will indirectly affect the concentration change of DO index;3)Indicator The degree of impact on water quality is represented by COD_Mn>NH₃-N>TN;4)Analysis of the importance of water quality factors.When the water quality is relatively clean,the main impact factor of the water body is COD_Mn.When poor,TN is a major contributor to water quality in the river section;5)Taking the water quality of the section of the wood town in 2016 as an example,the relevant indicators are graded and the correct rate is more than 83.33%,the conclusions reliable.The method can judge the water quality category and grasp the pollution situation of the river quickly and conveniently.It has changed the one-sidedness and pessimism and guided the river water quality control.This method predicts higher accuracy of related unknown indicators,which compensates the lack of water quality information and provides a new idea for watershed water environment management.