Forecast Zoning of Forest Fire Occurrence: A Case Study in Southern China

Abstract

Forest fires in the southern region of China pose significant threats to ecological balance, human safety, and socio-economic stability. Forecast zoning the occurrence of these fires is crucial for timely and effective response measures. This study employs the random forest algorithm and geospatial analysis, including kernel density and standard deviation ellipse methods, to predict forest fire occurrences. Historical fire data analysis reveals noteworthy findings: (i) Decreasing Trend in Forest Fires: The annual forest fire count in the southern region exhibits a decreasing trend from 2001 to 2019, indicating a gradual reduction in fire incidence. Spatial autocorrelation in fire point distribution is notably observed. (ii) Excellent Performance of Prediction Model: The constructed forest fire prediction model demonstrates outstanding performance metrics, achieving high accuracy, precision, recall, F1-scores, and AUC on the testing dataset. (iii) Seasonal Variations in High-Risk Areas: The probability of high-risk areas for forest fires in the southern region shows seasonal variations across different months. Notably, March to May sees increased risk in Guangxi, Guangdong, Hunan, and Fujian. June to August concentrates risk in Hunan and Jiangxi. September to November and December to February have distinct risk zones. These findings offer detailed insights into the seasonal variations of fire risk, providing a scientific basis for the prevention and control of forest fires in the southern region of China.

Keywords

• forest fire prediction
• southern China
• geospatial analysis
• machine learning
• prevention and control