{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T04:59:05Z","timestamp":1775019545167,"version":"3.50.1"},"reference-count":92,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T00:00:00Z","timestamp":1705276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University, Riyadh, Saudi Arabia","award":["RSP2024R425"],"award-info":[{"award-number":["RSP2024R425"]}]},{"name":"King Saud University, Riyadh, Saudi Arabia","award":["NRF-2021R1A2C1003540"],"award-info":[{"award-number":["NRF-2021R1A2C1003540"]}]},{"name":"Korea government (MSIT)","award":["RSP2024R425"],"award-info":[{"award-number":["RSP2024R425"]}]},{"name":"Korea government (MSIT)","award":["NRF-2021R1A2C1003540"],"award-info":[{"award-number":["NRF-2021R1A2C1003540"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flooding is a natural disaster that coexists with human beings and causes severe loss of life and property worldwide. Although numerous studies for flood susceptibility modelling have been introduced, a notable gap has been the overlooked or reduced consideration of the uncertainty in the accuracy of the produced maps. Challenges such as limited data, uncertainty due to confidence bounds, and the overfitting problem are critical areas for improving accurate models. We focus on the uncertainty in susceptibility mapping, mainly when there is a significant variation in the predictive relevance of the predictor factors. It is also noted that the receiver operating characteristic (ROC) curve may not accurately depict the sensitivity of the resulting susceptibility map to overfitting. Therefore, reducing the overfitting problem was targeted to increase accuracy and improve processing time in flood prediction. This study created a spatial repository to test the models, containing data from historical flooding and twelve topographic and geo-environmental flood conditioning variables. Then, we applied random forest (RF) and extreme gradient boosting (XGB) algorithms to map flood susceptibility, incorporating a variable drop-off in the empirical loop function. The results showed that the drop-off loop function was a crucial method to resolve the model uncertainty associated with the conditioning factors of the susceptibility modelling and methods. The results showed that approximately 8.42% to 9.89% of Marib City and 9.93% to 15.69% of Shibam City areas were highly vulnerable to floods. Furthermore, this study significantly contributes to worldwide endeavors focused on reducing the hazards linked to natural disasters. The approaches used in this study can offer valuable insights and strategies for reducing natural disaster risks, particularly in Yemen.<\/jats:p>","DOI":"10.3390\/rs16020336","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T04:54:38Z","timestamp":1705294478000},"page":"336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Uncertainty Reduction in Flood Susceptibility Mapping Using Random Forest and eXtreme Gradient Boosting Algorithms in Two Tropical Desert Cities, Shibam and Marib, Yemen"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4190-771X","authenticated-orcid":false,"given":"Ali R.","family":"Al-Aizari","sequence":"first","affiliation":[{"name":"School of Environment, Northeast Normal University, Changchun 130024, China"},{"name":"Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China"},{"name":"Department of Geology and Environment, Thamar University, Dhamar P.O. Box 87246, Yemen"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4690-4983","authenticated-orcid":false,"given":"Hassan","family":"Alzahrani","sequence":"additional","affiliation":[{"name":"Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}]},{"given":"Omar F.","family":"AlThuwaynee","sequence":"additional","affiliation":[{"name":"Italian National Research Council, Research Institute for Geo-Hydrological Protection (CNR IRPI), Via Della Madonna Alta 126, I-06128 Perugia, Italy"}]},{"given":"Yousef A.","family":"Al-Masnay","sequence":"additional","affiliation":[{"name":"Department of Geology and Environment, Thamar University, Dhamar P.O. Box 87246, Yemen"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2880-0977","authenticated-orcid":false,"given":"Kashif","family":"Ullah","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5429-5180","authenticated-orcid":false,"given":"Hyuck-Jin","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Energy and Mineral Resources Engineering, Sejong University, 209 Neudong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea"}]},{"given":"Nabil M.","family":"Al-Areeq","sequence":"additional","affiliation":[{"name":"Department of Geology and Environment, Thamar University, Dhamar P.O. Box 87246, Yemen"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8402-156X","authenticated-orcid":false,"given":"Mahfuzur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh"},{"name":"Department of Civil Engineering, Kunsan National University, 558 Daehakro, Gunsan 54150, Republic of Korea"}]},{"given":"Bashar Y.","family":"Hazaea","sequence":"additional","affiliation":[{"name":"Department of Geology & Geophysics, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia"}]},{"given":"Xingpeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Environment, Northeast Normal University, Changchun 130024, China"},{"name":"State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, North-East Normal University, Changchun 130024, China"},{"name":"Key Laboratory for Vegetation Ecology, Ministry of Education, Changchun 130024, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1007\/s11069-018-03567-z","article-title":"Bin A systematic review on approaches and methods used for flood vulnerability assessment: Framework for future research","volume":"96","author":"Rehman","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_2","unstructured":"Shaw, R., Surjan, A., and Parvin, G.A. (2016). 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