{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:40:44Z","timestamp":1775756444929,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AXA Research Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Floods are one of the most destructive natural disasters, causing financial and human losses every year. As a result, reliable Flood Susceptibility Mapping (FSM) is required for effective flood management and reducing its harmful effects. In this study, a new machine learning model based on the Cascade Forest Model (CFM) was developed for FSM. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. The database included 21 flood-conditioning factors obtained from different sources. The performance of the proposed CFM was evaluated over two study areas, and the results were compared with those of other six machine learning methods, including Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Deep Neural Network (DNN), Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). The result showed CFM produced the highest accuracy compared to other models over both study areas. The Overall Accuracy (AC), Kappa Coefficient (KC), and Area Under the Receiver Operating Characteristic Curve (AUC) of the proposed model were more than 95%, 0.8, 0.95, respectively. Most of these models recognized the southwestern part of the Karun basin, northern and northwestern regions of the Gorganrud basin as susceptible areas.<\/jats:p>","DOI":"10.3390\/rs15010192","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:18:18Z","timestamp":1672370298000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":124,"title":["Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3678-4877","authenticated-orcid":false,"given":"Seyd Teymoor","family":"Seydi","sequence":"first","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9215-5354","authenticated-orcid":false,"given":"Yousef","family":"Kanani-Sadat","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7254-4475","authenticated-orcid":false,"given":"Mahdi","family":"Hasanlou","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}]},{"given":"Roya","family":"Sahraei","sequence":"additional","affiliation":[{"name":"School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14174-66191, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-2875","authenticated-orcid":false,"given":"Jocelyn","family":"Chanussot","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Grenoble INP, GIPSA-Lab, CNRS, University Grenoble Alpes, 38000 Grenoble, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"additional","affiliation":[{"name":"WSP Environment & Infrastructure Canada Limited, Ottawa, ON K2E 7L5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108999","DOI":"10.1016\/j.ecolind.2022.108999","article-title":"Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network","volume":"140","author":"Seydi","year":"2022","journal-title":"Ecol. 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