Research Of Air Quality Index Prediction Based On Improved Sparrow Search Algorithm

With the rapid development of Chinese economy and the continuous acceleration of urbanization,the air pollution problem has become more and more serious.In recent years,the frequent haze weather in many places not only has a great impact on people’s daily travel and urban traffic management,but also has a great harm to human health.As an important indicator of air quality,air quality index reflects the level of air pollution.Therefore,using scientific methods to predict the air quality index has important guiding significance for residents’ Healthy Travel and urban environmental governance.In order to predict the air quality index more accurately,based on the comprehensive consideration of various factors affecting the air quality index,We take the air quality index,pollutant concentration and meteorological condition data of Hangzhou from March 2021 to June 2022 as the research object,and proposes a method of Optimizing BP neural network based on the improved sparrow search algorithm.Firstly,the global search performance of sparrow search algorithm is used to optimize the weight and threshold of BP neural network,which solves the problems of slow convergence and easy to fall into local optimization in the process of predicting air quality index of traditional BP neural network.At the same time,aiming at the defects of sparrow search algorithm in the optimization process,chaos mapping and optimization strategy are introduced to enhance the global search and convergence ability of the algorithm and further improve the prediction performance.Then,the feature extraction ability of the grey correlation analysis method is used to extract the prediction index of model,and the BP neural network structure is determined.The air quality index prediction model(ISSA-BP)based on the combination of the improved sparrow search algorithm and the BP neural network to be optimized is constructed.Finally,ISSA-BP model is used to predict the air quality index of Hangzhou.The experimental results show that the prediction accuracy of this model has been significantly improved.Compared with other models,its prediction accuracy and goodness of fit are the best and the prediction error is the smallest.At the same time,it also provides a new prediction method for air pollution prevention and control.