基于特征提取和集成学习的雷电预测能力提升

雷电是对人类社会有重大安全影响的自然灾害之一, 对雷电进行监测、预警是降低其危害的重要手段。利用广州市黄埔区的大气电场仪资料和闪电定位资料统计分析了反映雷电趋势的相关特征, 并从中提取预警因子探讨与电场仪探测范围内雷电事件的相关性, 基于基分类器BP神经网络, 分别通过Bagging和Adaboost的方法建立集成模型。试验表明, 在以30分钟为时间片的雷电事件预警中, 基于BP神经网络模型, 对比本试验提取的特征和其他研究提供的特征, 误报率和漏报率分别降低了16.83%和15.19%;集成方法比未集成的单一BP神经网络误报率最大降低了11.46%, 漏报率最大降低了4.73%, 说明了特征提取和集成学习的方法能有效提升模型在雷电预测中的准确率。 

LIGHTNING PREDICTION CAPABILITY IMPROVEMENT BASED ON FEATURE EXTRACTION AND ENSEMBLE LEARNING

Lightning is one of the natural disasters that have a major impact on human safety. Monitoring and early warning of lightning are important means to reduce harm. The present study uses the atmospheric electric field data and lightning location data in Huangpu District of Guangzhou to analyze relevant features that reflect the lightning trend, find early warning factors from them, and explore their correlation with lightning events within the electric field detection range. The experiment is based on the base classifier BP neural network, and establishes the ensemble model through Bagging and Adaboost respectively. The experiments show that in the early warning of lightning event with a time window of 30 minutes, the features extracted in this experiment reduce FPR and FNR by 16.83% and 15.19% respectively, compared with the features provided by the current research. Compared with the single BP neural network, the ensemble method reduces FPR by 11.46% and FNR by 4.73%, which shows that the method of feature extraction and ensemble learning can effectively improve the accuracy of the model in lightning prediction.