Assessment of forest fire risk in Fuzhou City based on CatBoost Algorithm
In the report,based on a total of 23 mountain fire impact factors in four categories:vegetation,topog-raphy,meteorology,and human activity,a feature dataset was constructed,and a daily mountain fire risk as-sessment model for Fuzhou City based on the CatBoost integrated learning method was also constructed.The mountain fires in Fuzhou are spatially clustered and there is a significant downward trend of the number of moun-tain fires from 2010 to 2021;The occurrence of mountain fires in Fuzhou is most influenced by the normalized vegetation index,followed by meteorological,topographical,and human activity factors;The integrated learn-ing methods were generally more accurate in predicting mountain fires in Fuzhou City,the CatBoost mountain fire warning model outperforms the currently commonly used RF and XGBoost models in terms of probabilistic prediction and fire point identification with an AUC of 0.928.Based on the model,it was concluded that the risk of mountain fire in Fuzhou City decreases from its northeast and southwest to its central part,the mountain fire risk is relatively high in Luoyuan,Lianjiang,Minqing and Yongtai counties,and relatively low in urban ar-eas.Our study can assess the mountain fire risk level in Fuzhou City,and which can be used as a reference val-ue for prevention and control management of mountain fire in Fuzhou City.
mountain firespatial and temporal distributioninfluence factorsrisk assessmentensemble learning
NETL
NSTL
万方数据
