基于信号分解和深度学习的雷电预警方法OA

Atmospheric electric field is the most intuitive factor reflecting the weather changes,and it can provide effective support for the early warning of thunderstorms.The problems of modal overlapping phenomenon and difficulty of parameter searching in the empirical mo-dal decomposition of atmospheric electric field time series are analyzed and a hybrid lightning warning model based on improved modal de-composition and deep learning is established.The improved modal decomposition algorithm is utilized to process the time series of atmos-pheric electric field,the entropy value of the samples of the decomposed signals is calculated and classified into reconstructed signals,the hyper-parameter optimization and prediction of the prediction model are carried out,and the prediction curves of the atmospheric electric field are obtained by the fusion of the prediction results of the three types of reconstructed signals.The experimental results show that the evaluation index of the prediction results is 0.124 6 for MAPE,0.3402 for RMSE,and the value of R2 reaches 0.9883.By comparing the model constructed with other deep learning models for lightning warning,what's clear is that the proposed model has better prediction effect and can provide effective technical support for thunderstorm warning.