{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:47:15Z","timestamp":1775069235936,"version":"3.50.1"},"reference-count":99,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T00:00:00Z","timestamp":1667347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Interdisciplinary Research Centre for Membranes and Water Security"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Floods, one of the most common natural hazards globally, are challenging to anticipate and estimate accurately. This study aims to demonstrate the predictive ability of four ensemble algorithms for assessing flood risk. Bagging ensemble (BE), logistic model tree (LT), kernel support vector machine (k-SVM), and k-nearest neighbour (KNN) are the four algorithms used in this study for flood zoning in Jeddah City, Saudi Arabia. The 141 flood locations have been identified in the research area based on the interpretation of aerial photos, historical data, Google Earth, and field surveys. For this purpose, 14 continuous factors and different categorical are identified to examine their effect on flooding in the study area. The dependency analysis (DA) was used to analyse the strength of the predictors. The study comprises two different input variables combination (C1 and C2) based on the features sensitivity selection. The under-the-receiver operating characteristic curve (AUC) and root mean square error (RMSE) were utilised to determine the accuracy of a good forecast. The validation findings showed that BE-C1 performed best in terms of precision, accuracy, AUC, and specificity, as well as the lowest error (RMSE). The performance skills of the overall models proved reliable with a range of AUC (89\u201397%). The study can also be beneficial in flash flood forecasts and warning activity developed by the Jeddah flood disaster in Saudi Arabia.<\/jats:p>","DOI":"10.3390\/rs14215515","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:53:07Z","timestamp":1667447587000},"page":"5515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0618-8190","authenticated-orcid":false,"given":"Ahmed","family":"Al-Areeq","sequence":"first","affiliation":[{"name":"Interdisciplinary Research Center for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9356-2798","authenticated-orcid":false,"given":"S.","family":"Abba","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4885-916X","authenticated-orcid":false,"given":"Mohamed","family":"Yassin","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"given":"Mohammed","family":"Benaafi","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2842-6532","authenticated-orcid":false,"given":"Mustafa","family":"Ghaleb","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Intelligent Secure Systems (IRC-ISS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0714-8580","authenticated-orcid":false,"given":"Isam","family":"Aljundi","sequence":"additional","affiliation":[{"name":"Interdisciplinary Research Center for Membranes and Water Security (IRC-MWS), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia"},{"name":"Department of Chemical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106620","DOI":"10.1016\/j.ecolind.2020.106620","article-title":"GIS-Based Comparative Assessment of Flood Susceptibility Mapping Using Hybrid Multi-Criteria Decision-Making Approach, Na\u00efve Bayes Tree, Bivariate Statistics and Logistic Regression: A Case of Top\u013ea Basin, Slovakia","volume":"117","author":"Ali","year":"2020","journal-title":"Ecol. 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