Research On Data-Driven Water Quality Prediction Method And Development Of Monitoring System

Water resources are closely related to all life on earth.Modern industry and agriculture cannot continue to develop without water resources.Nowadays,with the development of economy,water pollution has become a global common problem.How to prevent and control water pollution is the key.Water quality time series data is a general storage form of water quality monitoring results,which contains not only the historical information of water environment system,but also the relationship information that promotes the dynamic evolution of water system.Therefore,learning and analyzing water quality time series data is helpful to master its evolution law,and then accurately predict the changes of water quality parameters,which is of great significance for the treatment and prevention of water environmental pollution.This paper focuses on the research and application of water quality time series prediction algorithm.Aiming at the multivariate water quality time series data with many uncertain time-varying characteristics and large amount of data and different application scenarios,this paper improves the basic prediction algorithm,enhances the accuracy and efficiency of prediction,and improves the generalization ability and practical application value of the model.Aiming at the problems of many parameters,complex process and difficult popularization of traditional water quality model,this paper uses echo state network to design prediction model and apply it to water quality time series data prediction.In order to solve the problem of poor generalization ability of echo state network and unable to effectively select the parameters of reserve pool according to different water quality time series data,a prediction model based on improved grasshopper optimization algorithm and echo state network is proposed.Using the grasshopper optimization algorithm of hybrid Cauchy mutation and differential evolution,the echo state network can adaptively select the network parameters and improve the network performance.Among them,in order to better balance the global search and local search,the decreasing coefficient c of grasshopper optimization algorithm can be adjusted adaptively with a new random strategy;The combination of Cauchy mutation operator and differential evolution operator can update the population position,which enhances the optimization performance of the algorithm.Through the above improvements,the prediction accuracy and generalization ability of the prediction model based on echo state network are enhanced.Aiming at the problem that the off-line prediction model has high complexity and poor real-time performance,and can not meet the needs of on-line prediction,the system uses the kernel recursive least square method to extract the nonlinear characteristics between water quality parameters for water quality prediction.At the same time,in order to solve the problems of large memory occupation and increasing space-time complexity caused by data compression when dealing with the prediction task of large-scale multivariate water quality data,a dynamic search sparse kernel recursive least square algorithm is proposed.The algorithm uses the approximate linear dependence judgment criterion and the fixed budget strategy to effectively restrict the sum matrix,so as to improve some problems of kernel recursive least square method.At the same time,aiming at the problem of uncertain threshold parameters in the thinning strategy,the swarm intelligence optimization algorithm is used to widely search the parameter solution space and find the most suitable parameter combination for the current prediction task,which greatly improves the prediction accuracy and generalization ability.In view of the complex and changeable conditions of water source reservoir and other environmental monitoring points,the environment is bad,and it is impossible to manually collect water quality data in real time,this paper designs a set of water quality monitoring system,which can maintain stable operation under complex working conditions.At the same time,as an application platform of prediction model,it realizes the online monitoring and prediction of water quality information of water source,which is conducive to researchers’ water quality management and decision-making,and has high application value.