Research And System Implementation Of Meteorological Elements And PM2.5 Forecast Model Based On Wavelet Denoising And Cyclic Neural Network

In a rapidly changing society,all aspects of human life are deeply affected by changes in meteorological factors and PM2.5.Therefore,accurate prediction of future meteorological elements and PM2.5 data is of great importance for many occasions requiring meteorological elements(such as agricultural areas,industrial areas,living areas,etc.).The data set of this paper is provided by School of Management,Statistical Science Center,Peking University,in UCI Machine Learning Knowledge Base.The main contents are relatively complete meteorological elements and PM2.5 data.Then,the data are processed preferentially through a series of operations as follows:first,the standard data,i.e.the complete preprocessing of meteorological elements and PM2.5 data sets;The second is to obtain the data to be predicted,that is,to extract the data with complete features.Thirdly,data analysis is carried out when more complete results are obtained.Then the finishing touch is to make a comprehensive and comprehensive prediction experiment with the meteorological elements and PM2.5 prediction model proposed in the hub chapter of this paper,which start with wavelet de-noising operation and take the cyclic neural network as the core.The experimental results show that compared with the traditional model,the model proposed in this paper can improve the accuracy of the prediction results of meteorological elements and PM2.5 and reduce the training time.It provides a profound reference for the method of predicting meteorological elements and also provides a reference for the development of meteorological data prediction models.First:preprocessing,feature extraction and data analysis of meteorological elements and PM2.5 data.The whole process includes the pretreatment processes of missing value processing,abnormal value processing,format processing and logic check,and feature selection is carried out in combination with relevant heat maps and actual conditions.Finally,the standard data to be predicted is compared with time series,box chart and line chart,and the meteorological elements related to time series and PM2.5 data analysis results are obtained,which provides the basis for meteorological elements and PM2.5.Secondly:a meteorological element prediction model based on wavelet denoising and cyclic neural network is proposed.The prediction model includes two parts:first,wavelet denoising is carried out on meteorological elements and PM2.5 data,and "learning" tasks are processed in advance to further reduce data noise and improve experimental accuracy;Secondly,the processed data are used as the inputs of the two-layer RNN,two-layer LSTM and two-layer GRU network models respectively to carry out the whole prediction process,which provides the basis for the experiment of meteorological elements and PM2.5 data prediction.Third,compared to the overall and comprehensive test of a comprehensive and comprehensive team of various excellent prediction models,this paper focuses on excellent models centered on circulatory neural networks,including wavelet noise removal operations,and compares them in detail with traditional models.Experimental results,for weather elements and PM2.5 data of different cities,wavelet noise removal operation and sequential neural network and the binding of the variants to prove that increases the prediction accuracy,the results are optimal.Fourthly:A weather element and PM2.5 data analysis and prediction system based on wavelet de-noising and cyclic neural network is designed and implemented.The whole system adopts python and Django framework,and develops a Web system combining back-end management module and front-end display module through environment.The final system runs well after testing and meets the actual needs.