Qiantang River Basin Water Quality Evaluation And Sudden Water Pollution Early Warning Methods

In recent years,as the Chinese government attaches great importance to water pollution control and the people’s awareness of environmental protection,the quality of the water environment has been greatly improved.However,the rapid development of industry led to many sudden water pollution accidents.How to minimize the losses caused by those accidents has become one of the research hotspots in the field of water environment.In view of this,based on the results of the characteristics of the water quality of the Qiantang River Basin,the water quality evaluation and the early warning methods of sudden water pollution accidents have been carried out.The specific research content is as follows:(1)Aiming at the problems of high indicator monitoring cost and poor indicator adaptability in water quality evaluation and water quality early warning,systematic cluster analysis and self-organizing mapping method based on the time and space characteristics of watershed water quality are proposed.The above methods are applied to analyze the water quality characteristics of the Qiantang River Basin,the results determine five water quality parameters as the indicators for the water quality evaluation and water quality warning of the basin.(2)Aiming at the problem of non-linearity and uncertainty of water quality parameters,a water quality evaluation model MA-ELM is proposed.The model uses MA to optimize the randomly generated input weights and hidden layer thresholds in the ELM model for improving its generalization ability.Compared with error back propagation neural network and traditional ELM,the results show that the stability and accuracy of the model are the best.The MA-ELM model is applied to evaluate the water quality of the Qiantang River Basin.The evaluation results show that the water quality in the upper reaches of the basin is better than that in the middle and lower reaches,and the water quality in spring and winter is better than that in summer and autumn,showing obvious temporal and spatial characteristics.(3)Aiming at the problem of poor accuracy of the sudden water pollution early warning model,a dynamic particle swarm algorithm combined with real-time data is proposed.The algorithm dynamically changes the inertia weight value according to the similarity between the particle and the group’s optimal historical position and the change of the search algebra.A sudden water pollution experiment is carried out in the Qiantang River Basin.The experimental results show that the early warning effect of the model is far better than that of the model without dynamic correction of parameters.(4)Engineering the water quality evaluation model and sudden water pollution early warning model studied to the river basin water environment monitoring and trend forecasting system platform.The platform takes the Qiantang River Basin as a specific application object,and currently has a good operating effect.