Prediction-Based Thunderstorm Path Recovery Method Using CNN-BiLSTM  

作　　者：Xu Yang Ling Zhuang Yuqiang Sun Wenjie Zhang 

机构地区：[1]School of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing,210044,China [2]Department of Electrical and Computer Engineering,University of Alberta,Edmonton,AB T6G 2R3,Canada [3]CCCC Highway Consultants Co.,Ltd.,Beijing,100101,China [4]School of Geographical Sciences,Nanjing University of Information Science and Technology,Nanjing,210044,China [5]State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing,100101,China

出　　处：《Intelligent Automation & Soft Computing》2023年第8期1637-1654,共18页智能自动化与软计算（英文）

基　　金：supported by a grant from State Key Laboratory of Resources and Environmental Information System,the National Natural Science Foundation of China,Grant Number 42201053;the Program of China Scholarship Council,Grant Number 202209040027;the Postgraduate Research&Practice Innovation Program of Jiangsu Province,Grant Number KYCX21_1000,which are highly appreciated by the authors.

摘　　要：The loss of three-dimensional atmospheric electric field(3DAEF)data has a negative impact on thunderstorm detection.This paper proposes a method for thunderstorm point charge path recovery.Based on the relation-ship between a point charge and 3DAEF,we derive corresponding localization formulae by establishing a point charge localization model.Generally,point charge movement paths are obtained after fitting time series localization results.However,AEF data losses make it difficult to fit and visualize paths.Therefore,using available AEF data without loss as input,we design a hybrid model combining the convolutional neural network(CNN)and bi-directional long short-term memory(BiLSTM)to predict and recover the lost AEF.As paths are not present during sunny weather,we propose an extreme gradient boosting(XGBoost)model combined with a stacked autoencoder(SAE)to further determine the weather conditions of the recovered AEF.Specifically,historical AEF data of known weathers are input into SAE-XGBoost to obtain the distribution of predicted values(PVs).With threshold adjustments to reduce the negative effects of invalid PVs on SAE-XGBoost,PV intervals corresponding to different weathers are acquired.The recovered AEF is then input into the fixed SAE-XGBoost model.Whether paths need to be fitted is determined by the interval to which the output PV belongs.The results confirm that the proposed method can effectively recover point charge paths,with a maximum path deviation of approximately 0.018 km and a determination coefficient of 94.17%.This method provides a valid reference for visual thunderstorm monitoring.

关 键 词：THUNDERSTORM point charge atmospheric electric field(AEF) RECOVERY 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]