Driving Factors and Forecasting Model of Lightning-Caused Forest Fires in Daxing' anling Mountains Based on Multi-Sources Data and Machine Learning Method

【Objective】Due to the complexity and strong concealment of lightning-caused forest fire occurrence, it is difficult to monitor and early warning. For most available forest fire forecasting models, although main meteorological factors are taken into account the models, their adaptability and precision are still relatively low. In this study, based on the long-term multi-sources data,the driving factors of lightning-caused forest fires were analyzed, and a dynamic lightning-caused fire forecasting model with high spatial resolution was built by using machine learning method, to provide support for the fire prevention and control.【Method】The spatial and temporal distribution of lightning-caused fires from 2010 to 2020 was analyzed. Multi-source data such as ground-based lightning observations, satellite data, meteorological reanalysis data and DEM data were used to extract 18 driving factors from 4 categories（i.e., lightning, meteorology, vegetation and terrain）. The characteristics of each driving factor and th