基于机器学习的目标点雷电安全风险预警方法研究OA北大核心CSTPCD

The present study aimed to develop an accurate lightning risk classification and warning model for target points by using 1404 sets of data from four types of historical thunderstorm processes in Guangdong.Four machine learning algorithms were employed,and seven forecast factors,such as the physical characteristics of lightning occurrence around the target point,the breeding environment of lightning hazard,and the characteristics of the disaster-bearing body,were adopted to conduct multi-index evaluation and analysis of each risk early warning model.The results showed that the random forest algorithm exhibited the highest early warning accuracy in both the no-level model(95%)and the four-level model(73%).In contrast,the traditional convolutional neural network model proved to be ineffective for this purpose.Canton Tower was selected as the target point for model feasibility verification,and a lightning safety risk warning grading model tailored to the characteristics of thunderstorms in Guangdong was obtained.Finally,based on the identified deficiencies in the research process,ideas and methods for future optimization were proposed.