Combining machine learning and multi-criteria decision analysis for forest fire susceptibility assessment

Forest fires harm forests and endanger lives. Susceptibility assessment mapping using remote sensing and geographic information systems can help prevent forest fires and allocate fire-fighting resources rationally. This study combines machine learning (ML) and multi-criteria decision analysis to propose LOGEAHP – an integrated method based on logistic regression and the analytic hierarchy process (AHP). This method is used to calculate the relative weights of forest fire influencing factors and generate a forest fire susceptibility map for the Greater Khingan Range. The results show that nearly one-third of the Greater Khingan Range faced severe high forest fire risks between 2013–2024, with higher fire risks in the northern and southern regions than those in the central region. “Distance from Road” is the most critical factor affecting forest fire risks in the Greater Khingan Range, with a feature value as high as 1.1287. The LOGEAHP exhibits excellent fire assessment performance and stability, with an average receiver operating characteristic (ROC) of 0.9311 and 95% confidence interval for ROC ranging from 0.91 to 0.95. Bootstrap validation indicates that LOGEAHP achieves a statistically significant improvement over ML and AHP, with p-value significantly less than 0.01. This approach provides a robust basis for local fire prevention strategies.