Prediction And Evaluation Of Chaohu Water Quality Based On Hybrid Mathematical Model

In recent years,with the rapid development on economy of China,there is a serious problem on water quality,which need to be monitored and protected.The correct assessment of water quality is important for the maintenance of water resources.In this paper,the Yuxikou section of Chaohu in the Yangtze river basin and the Binhu section of Heifei in the Huai river basin were used as the study areas to explore the issue of water quality.The main pollutants are dissolved oxygen(DO),ammonia nitrogen(CODMn)and permanganate(CODMn).The used data are weekly collected from the beginning of 2008 to the half of 2017.Considering that the water environment system is a complex nonlinear system,the prediction and the evaluation indicators in the research process will contain a large number of relevant nonlinear features.Therefore,a combined water quality model based on complementary integrated empirical mode decomposition and a support vector machines optimized by gray wolf optimization algorithm is proposed for prediction,and a T-S fuzzy neural network method is proposed for water quality assessment.The experimental results show that the algorithms presented in this paper are superior in applicability and error control.Before the water quality prediction model was put forward,the complementary integrated empirical mode decomposition(CEEMD)method,the gray wolf(GWO)optimization algorithm and the support vector machine(SVM)were introduced.Aimed at modeling using SVM,we must not only consider the noise influence of original sequence,but also consider the parameter selection.In the modeling process,CEEMD method is first used to solve the effect of the white noise generated by the original signal on the result.Then,using the gray wolf optimization algorithm to optimize the parameters of SVM,this algorithm converges faster than the traditional algorithms.Finally,we concluded that the GWO algorithm is more efficient than other algorithms in the SVM parameter optimization.Before presenting the water quality assessment model,the article introduced the artificial neural network(ANN)and fuzzy set theory,and effectively combined the two methods to build a T-S fuzzy neural network model for water quality assessment.The evaluation model uses DO,CODMn and NH3-N as a fuzzy system input to obtain the degree of membership of the actual observed values belonging to various types of water quality,and finally the water quality categories of test data are obtained.Because each node of the fuzzy neural network has practical meanings,it largely overcomes the shortcomings of ANN theory.Applying the proposed method to the assessment of water quality,it has satisfactory interpretability and reliability.