Forest fire susceptibility mapping using multi-criteria decision making and machine learning models in the Western...

Forest fires have significantly increased over the last decade due to shifts in rainfall patterns, warmer summers, and long spells of dry weather events in the coastal regions. Assessment of susceptibility to forest fires has become an important management tool for damage control before the occurrence of fires, which often spread very rapidly. In this context, the current study was undertaken with the aim to map forest areas susceptible to fire in the state of Goa (India) using remote sensing (RS) and geographic information system () derived variables through an analytical hierarchy process (AHP) and machine learning techniques namely random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB). Nine variables viz. Elevation (m), slope (%), aspect, topographic wetness index (TWI), forest cover types, average normalized difference vegetation index (NDVI), distance to road (m), distance to settlement (m), and land surface temperature (LST, °C) were used to map susceptible areas in five different classes. The map classified forest areas into different susceptibility levels, with significant variations observed across different models. The study emphasized the importance of machine learning techniques for forest management and fire risk assessment. Validation of the susceptibility map showed excellent performance of the models, with the random forest model exhibiting the best performance. The forest fire susceptibility map generated using RF indicated that a large area (44.15%) of forest cover in Goa is very highly susceptible to fire followed by highly susceptible (21.35%) and a moderately susceptible area of 15.62%. SHapley Additive exPlanations (SHAP) analysis using RF identified forest type, distance from settlement, slope and NDVI as important variables affecting forest fire susceptibility. In the study area, an extended dry period with no post-monsoon rainfall makes the forest highly susceptible to fire. In view of the large area potentially susceptible to forest fire, there is an urgent need to implement preventive measures for fire control in the identified zones.