Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing

Fires are one of the most destructive forces in natural ecosystems. This study aims to develop and compare four hybrid models using two well-known machine learning models, support vector regression (SVR) adaptive neuro-fuzzy inference system (ANFIS), as well meta-heuristic whale optimization algorithm (WOA) simulated annealing (SA) map wildland fires Jerash Province, Jordan. For modeling, 109 fire locations were used along with 14 relevant factors, including elevation, slope, aspect, land use, normalized difference vegetation index (NDVI), rainfall, temperature, wind speed, solar radiation, soil texture, topographic wetness (TWI), distance drainage, population density, variables affecting occurrence. The area under receiver operating characteristic (AUROC) was evaluate accuracy models. findings indicated that SVR-based yielded a higher AUROC value (0.965 0.949) than ANFIS-based (0.904 0.894, respectively). Wildland susceptibility maps can play major role shaping firefighting tactics.