{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T20:07:24Z","timestamp":1781899644121,"version":"3.54.5"},"reference-count":74,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/ijgi10060382","type":"journal-article","created":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T11:12:13Z","timestamp":1622805133000},"page":"382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Wildland Fire Susceptibility Mapping Using Support Vector Regression and Adaptive Neuro-Fuzzy Inference System-Based Whale Optimization Algorithm and Simulated Annealing"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3947-5284","authenticated-orcid":false,"given":"A\u2019kif","family":"Al-Fugara","sequence":"first","affiliation":[{"name":"Department of Surveying Engineering, Faculty of Engineering, Al Al-Bayt University, Mafraq 25113, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Nouh","family":"Mabdeh","sequence":"additional","affiliation":[{"name":"Department of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Ahmadlou","sequence":"additional","affiliation":[{"name":"GIS Department, Geodesy and Geomatics Faculty, K. N. Toosi University of Technology, Tehran 1996715433, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2328-2998","authenticated-orcid":false,"given":"Hamid Reza","family":"Pourghasemi","sequence":"additional","affiliation":[{"name":"Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz 71557-13876, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8980-6809","authenticated-orcid":false,"given":"Rida","family":"Al-Adamat","sequence":"additional","affiliation":[{"name":"Department of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Information, Systems & Modelling, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia"},{"name":"Department of Meteorology, King Abdulaziz University, P.O. Box 80234, Jeddah 21589, Saudi Arabia"},{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdel Rahman","family":"Al-Shabeeb","sequence":"additional","affiliation":[{"name":"Department of GIS and Remote Sensing, Institute of Earth and Environmental Sciences, Al Al-Bayt University, Mafraq 25113, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"ref_1","unstructured":"FAO, and UNEP (2020). The State of the World\u2019s Forests. Forests, Biodiversity and People, FAO."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Chuvieco, E., Allg\u00f6wer, B., and Salas, J. (2003). Integration of physical and human factors in fire danger assessment Wildland fire danger estimation and mapping: The role of remote sensing data. 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