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.

Cloud-to-Ground lightning nowcasting using Machine Learning

La Fata A.;Amato F.;Procopio R.;
2021

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1082941
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