Alice La Fata, Federico Amato, M. Bernardi, M. D’Andrea, R. Procopio, E. Fiori
{"title":"Cloud-to-Ground lightning nowcasting using Machine Learning","authors":"Alice La Fata, Federico Amato, M. Bernardi, M. D’Andrea, R. Procopio, E. Fiori","doi":"10.1109/ICLPandSIPDA54065.2021.9627428","DOIUrl":null,"url":null,"abstract":"This paper discusses the use of Random Forest (RF), a popular Machine Learning (ML) algorithm, to perform spatially explicit nowcasting of cloud-to-ground lightning occurrence. An application to the Italian territory and the surrounding seas is then presented. Specifically, a dataset including eighteen geo-environmental features has been used to forecast 1-hour ahead lightning occurrence over a three-months period (August- October 2018). The features' importance resulting from the best RF model showed how data-driven models are able to identify relationships between variables, in agreement with previous physically-based knowledge of the phenomenon. The encouraging results obtained in terms of forecasting accuracy suggest how, after proper improvements, ML-based algorithms could find their place in wider early-warning systems to support disaster risk management procedures.","PeriodicalId":70714,"journal":{"name":"中国防雷","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国防雷","FirstCategoryId":"1089","ListUrlMain":"https://doi.org/10.1109/ICLPandSIPDA54065.2021.9627428","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
This paper discusses the use of Random Forest (RF), a popular Machine Learning (ML) algorithm, to perform spatially explicit nowcasting of cloud-to-ground lightning occurrence. An application to the Italian territory and the surrounding seas is then presented. Specifically, a dataset including eighteen geo-environmental features has been used to forecast 1-hour ahead lightning occurrence over a three-months period (August- October 2018). The features' importance resulting from the best RF model showed how data-driven models are able to identify relationships between variables, in agreement with previous physically-based knowledge of the phenomenon. The encouraging results obtained in terms of forecasting accuracy suggest how, after proper improvements, ML-based algorithms could find their place in wider early-warning systems to support disaster risk management procedures.