Application of lightning spatio-temporal localization method based on deep LSTM and interpolation

Lightning is a strong discharge phenomenon that occurs in nature and poses a great threat to people’s property and life safety. The generation of lightning originates from the continuous accumulation of electric charges in clouds, and the atmospheric electric field instrument, as a measurement device reflecting the most fundamental cause of lightning generation, is used to detect the occurrence of lightning, which has been very widely used due to its low price and easy installation. However, its detection results are directionless and the detection range is limited. Therefore, this paper proposed a method for spatio-temporal localization of lightning based on deep Long Short-Term Memory (LSTM) neural network and interpolation method. The time series data of electric field detected by 30 atmospheric electric field instruments was fed into deep LSTM network for training, and the prediction results were classified into five categories according to the time period of lightning occurrence by softmax function. Furthermore, data from the networked stations were interpolated using ordinary Kriging (OK) to obtain the electric potential distribution in Guangzhou city, which was used to infer the approximate area where lightning may occur. The above two algorithms passed the accuracy test respectively. Finally, two case studies were done based on LSTM-OK. The results show that it can obtain satisfactory prediction performance.