Abstract
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) and the adaptive neuro-fuzzy inference system (ANFIS), as well as two meta-heuristic models, the whale optimization algorithm (WOA) and simulated annealing (SA) to map wildland fires in 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 index (TWI), distance to drainage, and population density, as the variables affecting the fire occurrence. The area under the receiver operating characteristic (AUROC) was used to evaluate the accuracy of the models. The findings indicated that SVR-based hybrid models yielded a higher AUROC value (0.965 and 0.949) than the ANFIS-based hybrid models (0.904 and 0.894, respectively). Wildland fire susceptibility maps can play a major role in shaping firefighting tactics.
Highlights
In the past decades, wildfires and forest fires have been one of the major and most pervasive hazards in destroying natural ecosystems
The major factors selected by the support vector regression (SVR)-whale optimization algorithm (WOA) model were solar radiation, population density, topographic wetness index (TWI), distance to drainage, temperature, rainfall, normalized difference vegetation index (NDVI), land use, slope, and elevation
The factors selected by SVR-simulated annealing (SA) were radiation, population density, TWI, distance to drainage, temperature, rainfall, NDVI, land use, slope, aspect, and elevation
Summary
Wildfires and forest fires have been one of the major and most pervasive hazards in destroying natural ecosystems. Millions of hectares of rangelands and forests are destroyed by fire worldwide [1]. Global warming and climate change, deforestation, land management decisions, insufficient precipitation, hot winds, litter accumulation, and friction between dry litter are among the factors that can cause natural fires in rangelands and forests [2,3]. To identify fire-susceptible zones, it is necessary to determine the factors affecting the occurrence of fire, such as fuels, topographic and climatic conditions, and human factors [8]. The relationship between these factors and the occurrence of fire must be determined For this purpose, it is necessary to record areas where fires occurred in the past and match them with the layers of the factors affecting fire susceptibility to determine the relationship between them [9]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
