{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T02:21:49Z","timestamp":1776306109921,"version":"3.50.1"},"reference-count":122,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,13]],"date-time":"2019-12-13T00:00:00Z","timestamp":1576195200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), na\u00efve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.<\/jats:p>","DOI":"10.3390\/rs11242995","type":"journal-article","created":{"date-parts":[[2019,12,13]],"date-time":"2019-12-13T11:27:22Z","timestamp":1576236442000},"page":"2995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5672-8525","authenticated-orcid":false,"given":"Omid","family":"Rahmati","sequence":"first","affiliation":[{"name":"Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam"},{"name":"Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9664-8770","authenticated-orcid":false,"given":"Omid","family":"Ghorbanzadeh","sequence":"additional","affiliation":[{"name":"Department of Geoinformatics\u2014Z_GIS, University of Salzburg, 5020 Salzburg, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1392-4275","authenticated-orcid":false,"given":"Teimur","family":"Teimurian","sequence":"additional","affiliation":[{"name":"Faculty of Natural Resources, University of Tehran, Karaj 31587-77871, Iran"}]},{"given":"Farnoush","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Faculty of Natural Resources, University of Tehran, Karaj 31587-77871, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9342-6550","authenticated-orcid":false,"given":"John P.","family":"Tiefenbacher","sequence":"additional","affiliation":[{"name":"Department of Geography, Texas State University, San Marcos, TX 78666, USA"}]},{"given":"Fatemeh","family":"Falah","sequence":"additional","affiliation":[{"name":"Department of Watershed Management, Faculty of Natural Resources and Agriculture, Lorestan University, Khorramabad 68151-44316, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3177-037X","authenticated-orcid":false,"given":"Saied","family":"Pirasteh","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Xipu Campus, Chengdu 611756, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6574-5762","authenticated-orcid":false,"given":"Phuong-Thao Thi","family":"Ngo","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5161-6479","authenticated-orcid":false,"given":"Dieu Tien","family":"Bui","sequence":"additional","affiliation":[{"name":"GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, N-3800 B\u00f8 i Telemark, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1080\/17445647.2016.1262794","article-title":"Snow avalanche hazard of the Krkono\u0161e National Park, Czech Republic","volume":"13","author":"Blahut","year":"2017","journal-title":"J. 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