{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:53:30Z","timestamp":1781585610858,"version":"3.54.5"},"reference-count":69,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,23]],"date-time":"2023-07-23T00:00:00Z","timestamp":1690070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["42175177"],"award-info":[{"award-number":["42175177"]}]},{"name":"Natural Science Foundation of China","award":["U2240216"],"award-info":[{"award-number":["U2240216"]}]},{"name":"Natural Science Foundation of China","award":["92047203"],"award-info":[{"award-number":["92047203"]}]},{"name":"Natural Science Foundation of China","award":["42075191"],"award-info":[{"award-number":["42075191"]}]},{"name":"Natural Science Foundation of China","award":["Y521002"],"award-info":[{"award-number":["Y521002"]}]},{"name":"Natural Science Foundation of China","award":["2019YFC1510204"],"award-info":[{"award-number":["2019YFC1510204"]}]},{"name":"Special Basic Research Key Fund for Central Public Scientific Research Institutes","award":["42175177"],"award-info":[{"award-number":["42175177"]}]},{"name":"Special Basic Research Key Fund for Central Public Scientific Research Institutes","award":["U2240216"],"award-info":[{"award-number":["U2240216"]}]},{"name":"Special Basic Research Key Fund for Central Public Scientific Research Institutes","award":["92047203"],"award-info":[{"award-number":["92047203"]}]},{"name":"Special Basic Research Key Fund for Central Public Scientific Research Institutes","award":["42075191"],"award-info":[{"award-number":["42075191"]}]},{"name":"Special Basic Research Key Fund for Central Public Scientific Research Institutes","award":["Y521002"],"award-info":[{"award-number":["Y521002"]}]},{"name":"Special Basic Research Key Fund for Central Public Scientific Research Institutes","award":["2019YFC1510204"],"award-info":[{"award-number":["2019YFC1510204"]}]},{"name":"National Key Research and Development Program of China","award":["42175177"],"award-info":[{"award-number":["42175177"]}]},{"name":"National Key Research and Development Program of China","award":["U2240216"],"award-info":[{"award-number":["U2240216"]}]},{"name":"National Key Research and Development Program of China","award":["92047203"],"award-info":[{"award-number":["92047203"]}]},{"name":"National Key Research and Development Program of China","award":["42075191"],"award-info":[{"award-number":["42075191"]}]},{"name":"National Key Research and Development Program of China","award":["Y521002"],"award-info":[{"award-number":["Y521002"]}]},{"name":"National Key Research and Development Program of China","award":["2019YFC1510204"],"award-info":[{"award-number":["2019YFC1510204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk.<\/jats:p>","DOI":"10.3390\/rs15143678","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T01:12:28Z","timestamp":1690161148000},"page":"3678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Urban Flood Risk Assessment through the Integration of Natural and Human Resilience Based on Machine Learning Models"],"prefix":"10.3390","volume":"15","author":[{"given":"Wenting","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"},{"name":"The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongzhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China"},{"name":"The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, NHRI, Nanjing 210029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingnan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixuan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"110521","DOI":"10.1016\/j.jenvman.2020.110521","article-title":"Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data","volume":"268","author":"Zhi","year":"2020","journal-title":"J. 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