Research On Time Series Prediction Method Of Waste Gas Pollution In Industrial Parks Based On Deep Learning

In recent years,with the continuous enhancement of industrial production and life,air pollution has become increasingly serious.In response to the new situation and needs of the industrial modernization transformation,intelligently predict the concentration of pollutants in the industrial park in the atmosphere,and provide an important scientific basis for the industrial park to take preventive measures in advance to effectively control and treat air pollution.In various systems,the time series actually obtained are generally non-stationary,noisy,and complex data affected by multiple factors,which have the characteristics of not obvious trends.In order to better solve the above-mentioned problems in multivariable nonlinear time series,the use of real-time data provided in natural systems and the addition of intelligent prediction methods can predict the concentration of pollutants in advance,provide accurate prevention and control countermeasures,and control air pollution in time,and repair the problematic links in industrial production and create a green artificial natural system with elastic,plastic and renewable functions.Aiming at the problem of industrial park waste gas pollution prediction,this paper optimizes the prediction model by using the atmospheric data of Taiyuan Environmental Testing Center and the time series prediction method based on deep learning,so as to improve the prediction accuracy:1.The overall design of the unary time series prediction model is carried out.The EMD-FUSION model combined with the hybrid deep network was established,the modal decomposition and feature self-organization were carried out,the components were predicted by the neural network,and the errors were compared with ARIMA,BP,LSTM and GRU models respectively.2.The overall design of the multivariate time series forecasting model is carried out.A hybrid CNN+BILSTM network was established,and multivariate time-series correlation variables were analyzed and STL decomposition was carried out.The errors were compared with those of traditional prediction models and undecomposed hybrid models respectively.3.Realize the prediction system of waste gas pollution in industrial parks.Starting from business management requirements and system functions,the demand analysis of the exhaust gas pollution forecasting system was carried out,and the system framework was designed to realize the system functions.