Prediction of Forest Fire Occurrence in Southwestern China

Southwestern China is an area heavily affected by forest fires, having a complex combination of fire sources and a high degree of human interference. The region is characterized by karst topography and a mixture of agricultural and forested areas, as well as diverse and dynamic mountainous terrain. Analyzing the driving factors behind forest fire occurrences in this area and conducting fire risk zoning are of significant importance in terms of implementing effective forest fire management. The Light Gradient Boosting Machine (LightGBM) model offers advantages in terms of efficiency, low memory usage, accuracy, scalability, and robustness, making it a powerful predictive algorithm that can handle large-scale data and complex problems. In this study, we used nearly 20 years of forest fire data in Southwestern China as the data source. Using mathematical statistics and kernel density analysis, we studied the spatiotemporal distribution characteristics of forest fires in Southwestern China. Considering 16 variables, including climate, vegetation, human factors, and topography, we employed the LightGBM model to predict and zone forest fire occurrences in Southwestern China. The results indicated the following conclusions: (i) Forest fires in Southwestern China are primarily concentrated in certain areas of Sichuan Province (such as Liangshan Yi Autonomous Prefecture and Panzhihua City), Guizhou Province (such as Qiannan Buyi and Miao Autonomous Prefecture), Yunnan Province (such as Puer City, Xishuangbanna Dai Autonomous Prefecture, and Wenshan Zhuang and Miao Autonomous Prefecture), and Chongqing Municipality. (ii) In terms of seasonality, forest fires are most frequent during the spring and winter, followed by the autumn and summer. (iii) The LightGBM forest fire prediction model yielded good results, having a training set accuracy of 83.088080%, a precision of 81.272437%, a recall of 88.760399%, an F1 score of 84.851539%, and an AUC of 91.317430%. The testing set accuracy was 79.987694%, precision was 78.541074%, recall was 85.978470%, F1 score was 82.091662%, and AUC was 87.977684%. These findings demonstrate the effectiveness of the LightGBM model in predicting forest fires in Southwest China, providing valuable insights regarding forest fire management and prevention efforts in the area.