Forest fire susceptibility modeling using hybrid approaches

Abstract

AbstractForest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to forest fires over Nowshahr County in Iran, using an integrated approach of index of entropy (IOE) with fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with a comparison of their precision. The spatial database incorporated the inventory of forest fire and conditioning factors. As a whole, 41 forest fire locations were identified. Out of these, 29 locations (≈70%) were randomly chosen for the forest fire susceptibility modeling (FFSM), and the remaining 12 locations (≈30%) were utilized for the validation of the models. Subsequently, utilizing FMV‐IOE, FR‐IOE, and IV‐IOE models, forest fire susceptibility maps were acquired. Finally, the modeling ability of the models for FFSM was assessed using an area under the receiver operating characteristic (AUROC) curve. The results manifested that the prediction accuracy of the FMV‐IOE model is slightly higher than that of the FR‐IOE and IV‐IOE models. The incorporation of IOE with FMV, FR, and IV models had AUROC values of 0.890, 0.887, and 0.878, respectively. The resulting FFSM can be effective in fire repression resource planning, sustainable development, and primary warning in regions with similar conditions.