Study on Small-Scale Forest Fire Risk Zoning Based on Random Forest and the Fuzzy Analytic Network Process

Forest fire risk mapping is an essential measure for forest fire management. Quickly and precisely assessing forest fire risks, rationally planning fire risk zones, and scientifically allocating firefighting resources are of great significance for mitigating the increasingly severe threat of forest fires. This study utilized the random forest (RF) algorithm and the Fuzzy Analytic Network Process (FANP) to conduct a forest fire risk-zoning study in the protection and development belt of Wuyishan National Park. The findings revealed that some areas in the western and southern parts of this region have relatively high fire risk levels. Particularly, forest fire prevention and control in the western area need to be strengthened to prevent potential hazards to Wuyishan National Park. The accuracy of the FANP model was as high as 88.5%; areas with fire risk levels of grade 3 and above could control 98.44% of forest fires, and the proportion of areas with fire risk levels of grade 4 and above was 33.41%, which could control 65.63% of forest fires. This finding indicates that the FANP has preferable applicability in small-scale forest fire risk zoning and can offer more reliable decision-making support and reference basis for regional forest fire management.