{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T06:20:17Z","timestamp":1775197217298,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFC3008502"],"award-info":[{"award-number":["2023YFC3008502"]}]},{"name":"National Key Research and Development Program of China","award":["52179003"],"award-info":[{"award-number":["52179003"]}]},{"name":"National Key Research and Development Program of China","award":["51879008"],"award-info":[{"award-number":["51879008"]}]},{"name":"National Key Research and Development Program of China","award":["72091511"],"award-info":[{"award-number":["72091511"]}]},{"name":"National Key Research and Development Program of China","award":["BNUXKJC2325"],"award-info":[{"award-number":["BNUXKJC2325"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFC3008502"],"award-info":[{"award-number":["2023YFC3008502"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52179003"],"award-info":[{"award-number":["52179003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51879008"],"award-info":[{"award-number":["51879008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72091511"],"award-info":[{"award-number":["72091511"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BNUXKJC2325"],"award-info":[{"award-number":["BNUXKJC2325"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Normal University Interdisciplinary Fund Project","award":["2023YFC3008502"],"award-info":[{"award-number":["2023YFC3008502"]}]},{"name":"Beijing Normal University Interdisciplinary Fund Project","award":["52179003"],"award-info":[{"award-number":["52179003"]}]},{"name":"Beijing Normal University Interdisciplinary Fund Project","award":["51879008"],"award-info":[{"award-number":["51879008"]}]},{"name":"Beijing Normal University Interdisciplinary Fund Project","award":["72091511"],"award-info":[{"award-number":["72091511"]}]},{"name":"Beijing Normal University Interdisciplinary Fund Project","award":["BNUXKJC2325"],"award-info":[{"award-number":["BNUXKJC2325"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, utilizing remote sensing data and limited historical inundation records. In this study, two ensemble learning algorithms, Random Forest (RF) and XGBoost, were adopted to assess the flood susceptibility of Kunming, a typical mountainous urban area prone to severe flood disasters. A flood inventory was created using flood observations from 2018 to 2022. The spatial database included 10 explanatory factors, encompassing climatic, geomorphic, and anthropogenic factors. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were selected for model comparison. To minimize the influence of expert opinions on model training, this study employed a strategy of uniformly random sampling in historically non-flooded areas for negative sample selection. The results demonstrated that (1) ensemble learning algorithms offer higher accuracy than other machine learning methods, with RF achieving the highest accuracy, evidenced by an area under the curve (AUC) of 0.87, followed by XGBoost at 0.84, surpassing both ANN (0.83) and SVM (0.82); (2) the interpretability of ensemble learning highlighted the differences in the potential distribution of the training data\u2019s positive and negative samples. Feature importance in ensemble learning can be utilized to minimize human bias in the collection of flooded-site samples, more targeted flood susceptibility maps of the study area\u2019s road network were obtained; and (3) ensemble learning algorithms exhibited greater stability and robustness in datasets with varied negative samples, as evidenced by their performance in F1-Score, Kappa, and AUC metrics. This paper further substantiates the superiority of ensemble learning in flood susceptibility assessment tasks from the perspectives of accuracy, interpretability, and robustness, enhances the understanding of the impact of negative samples on such assessments, and optimizes the specific process for urban flood susceptibility assessment using data-driven methods.<\/jats:p>","DOI":"10.3390\/rs16020320","type":"journal-article","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T09:24:11Z","timestamp":1705051451000},"page":"320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["Flood Susceptibility Assessment with Random Sampling Strategy in Ensemble Learning (RF and XGBoost)"],"prefix":"10.3390","volume":"16","author":[{"given":"Hancheng","family":"Ren","sequence":"first","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China"}]},{"given":"Bo","family":"Pang","sequence":"additional","affiliation":[{"name":"College of Water Sciences, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing 100875, China"}]},{"given":"Ping","family":"Bai","sequence":"additional","affiliation":[{"name":"Kunming Flood Control and Drought Relief Headquarters Office, Kunming 650000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0278-502X","authenticated-orcid":false,"given":"Gang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Industrial Science, University of Tokyo, Tokyo 153\u22128505, Japan"}]},{"given":"Shu","family":"Liu","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"},{"name":"Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038, China"}]},{"given":"Yuanyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"},{"name":"Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038, China"}]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"China Institute of Water Resources and Hydropower Research, Beijing 100038, China"},{"name":"Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, Beijing 100038, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.piutam.2015.06.003","article-title":"Hydro-Meteorological Disasters: Causes, Effects and Mitigation Measures with Special Reference to Early Warning with Data Driven Approaches of Forecasting","volume":"17","author":"Jayawardena","year":"2015","journal-title":"Procedia IUTAM"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1080\/1573062X.2013.857421","article-title":"Urban Flood Impact Assessment: A State-of-the-Art Review","volume":"12","author":"Hammond","year":"2015","journal-title":"Urban Water J."},{"key":"ref_3","first-page":"e00269","article-title":"A Review of the Current Status of Flood Modelling for Urban Flood Risk Management in the Developing Countries","volume":"7","author":"Nkwunonwo","year":"2020","journal-title":"Sci. Afr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20190216","DOI":"10.1098\/rsta.2019.0216","article-title":"Drivers of Future Urban Flood Risk","volume":"378","author":"Thorne","year":"2020","journal-title":"Philos. Trans. R. Soc. A"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.ejrh.2017.06.006","article-title":"The Impacts of Urbanisation and Climate Change on Urban Flooding and Urban Water Quality: A Review of the Evidence Concerning the United Kingdom","volume":"12","author":"Miller","year":"2017","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geomorph.2016.07.008","article-title":"Floods in Mountain Environments: A Synthesis","volume":"272","author":"Stoffel","year":"2016","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s11069-021-04715-8","article-title":"A Review on Applications of Urban Flood Models in Flood Mitigation Strategies","volume":"108","author":"Qi","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4399","DOI":"10.1007\/s11269-020-02567-8","article-title":"Land Cover Change and Flood Risk in a Peri-Urban Environment of the Metropolitan Area of Rome (Italy)","volume":"34","author":"Recanatesi","year":"2020","journal-title":"Water Resour. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"180309","DOI":"10.1038\/sdata.2018.309","article-title":"GFPLAIN250m, a Global High-Resolution Dataset of Earth\u2019s Floodplains","volume":"6","author":"Nardi","year":"2019","journal-title":"Sci. Data"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"139","DOI":"10.2747\/1548-1603.49.1.139","article-title":"LIDAR Data and Hydrological Applications at the Basin Scale","volume":"49","author":"Petroselli","year":"2012","journal-title":"GIScience Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1002\/hyp.6669","article-title":"At What Scales Do Climate Variability and Land Cover Change Impact on Flooding and Low Flows?","volume":"21","author":"Bonell","year":"2007","journal-title":"Hydrol. Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.wse.2019.03.001","article-title":"Performance Assessment of Two-Dimensional Hydraulic Models for Generation of Flood Inundation Maps in Mountain River Basins","volume":"12","author":"Pinos","year":"2019","journal-title":"Water Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1007\/s13201-020-01308-x","article-title":"Influence of Rain Pattern on Flood Control in Mountain Creek Areas: A Case Study of Northern Zhejiang","volume":"10","author":"Cao","year":"2020","journal-title":"Appl. Water Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s11069-021-05182-x","article-title":"Impact of Rainstorm Patterns on the Urban Flood Process Superimposed by Flash Floods and Urban Waterlogging Based on a Coupled Hydrologic\u2013Hydraulic Model: A Case Study in a Coastal Mountainous River Basin within Southeastern China","volume":"112","author":"Jiang","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"129446","DOI":"10.1016\/j.jhydrol.2023.129446","article-title":"Performance of the Flood Models in Different Topographies","volume":"620","author":"Moghim","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"130040","DOI":"10.1016\/j.jhydrol.2023.130040","article-title":"An Urban Hydrological Model for Flood Simulation in Piedmont Cities: Case Study of Jinan City, China","volume":"625","author":"Zhao","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2023WR034599","DOI":"10.1029\/2023WR034599","article-title":"Toward Street-Level Nowcasting of Flash Floods Impacts Based on HPC Hydrodynamic Modeling at the Watershed Scale and High-Resolution Weather Radar Data","volume":"59","author":"Costabile","year":"2023","journal-title":"Water Resour. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pham, B.T., Avand, M., Janizadeh, S., Phong, T.V., Al-Ansari, N., Ho, L.S., Das, S., Le, H.V., Amini, A., and Bozchaloei, S.K. (2020). GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment. Water, 12.","DOI":"10.3390\/w12030683"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.envsoft.2017.06.012","article-title":"A Novel Hybrid Artificial Intelligence Approach for Flood Susceptibility Assessment","volume":"95","author":"Chapi","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.jhydrol.2019.02.034","article-title":"A New Approach to Flood Susceptibility Assessment in Data-Scarce and Ungauged Regions Based on GIS-Based Hybrid Multi Criteria Decision-Making Method","volume":"572","author":"Arabsheibani","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1007\/s11069-017-2828-0","article-title":"GIS-Based Flood Risk Assessment in Suburban Areas: A Case Study of the Fangshan District, Beijing","volume":"87","author":"Hu","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Khoirunisa, N., Ku, C.-Y., and Liu, C.-Y. (2021). A GIS-Based Artificial Neural Network Model for Flood Susceptibility Assessment. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18031072"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s12517-018-3584-5","article-title":"Mapping Flood Susceptibility in an Arid Region of Southern Iraq Using Ensemble Machine Learning Classifiers: A Comparative Study","volume":"11","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s41748-019-00123-y","article-title":"Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-Criteria Decision Analysis","volume":"3","author":"Rahman","year":"2019","journal-title":"Earth Syst. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"134413","DOI":"10.1016\/j.scitotenv.2019.134413","article-title":"A Novel Deep Learning Neural Network Approach for Predicting Flash Flood Susceptibility: A Case Study at a High Frequency Tropical Storm Area","volume":"701","author":"Bui","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.scitotenv.2018.12.217","article-title":"Assessment of Urban Flood Susceptibility Using Semi-Supervised Machine Learning Model","volume":"659","author":"Zhao","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2608","DOI":"10.2166\/wcc.2021.051","article-title":"Application of Machine Learning Algorithms for Flood Susceptibility Assessment and Risk Management","volume":"12","author":"Madhuri","year":"2021","journal-title":"J. Water Clim. Chang."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"125235","DOI":"10.1016\/j.jhydrol.2020.125235","article-title":"Urban Flood Susceptibility Assessment Based on Convolutional Neural Networks","volume":"590","author":"Zhao","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.catena.2014.10.017","article-title":"Flood Susceptibility Assessment Using GIS-Based Support Vector Machine Model with Different Kernel Types","volume":"125","author":"Tehrany","year":"2015","journal-title":"Catena"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s11269-019-02301-z","article-title":"Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models-a Useful Tool for Flood Risk Management","volume":"33","author":"Costache","year":"2019","journal-title":"Water Resour. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5479","DOI":"10.1080\/10106049.2021.1920636","article-title":"Flash-Flood Susceptibility Mapping Based on XGBoost, Random Forest and Boosted Regression Trees","volume":"37","author":"Abedi","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.jhydrol.2019.03.073","article-title":"A Comparative Assessment of Flood Susceptibility Modeling Using Multi-Criteria Decision-Making Analysis and Machine Learning Methods","volume":"573","author":"Khosravi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MCG.2010.18","article-title":"Visual Classification: Expert Knowledge Guides Machine Learning","volume":"30","author":"MacInnes","year":"2010","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Belton, V., and Stewart, T. (2002). Multiple Criteria Decision Analysis: An Integrated Approach, Springer Science & Business Media.","DOI":"10.1007\/978-1-4615-1495-4"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112810","DOI":"10.1016\/j.jenvman.2021.112810","article-title":"Towards Better Flood Risk Management: Assessing Flood Risk and Investigating the Potential Mechanism Based on Machine Learning Models","volume":"293","author":"Chen","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhao, J., Wang, J., Abbas, Z., Yang, Y., and Zhao, Y. (2023). Ensemble Learning Analysis of Influencing Factors on the Distribution of Urban Flood Risk Points: A Case Study of Guangzhou, China. Front. Earth Sci., 11.","DOI":"10.3389\/feart.2023.1042088"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhou, Z.-H., and Zhou, Z.-H. (2021). Ensemble Learning, Springer.","DOI":"10.1007\/978-981-15-1967-3_8"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sagi, O., and Rokach, L. (2018). Ensemble Learning: A Survey. WIREs Data Min. Knowl. Discov., 8.","DOI":"10.1002\/widm.1249"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1007\/s11704-019-8208-z","article-title":"A Survey on Ensemble Learning","volume":"14","author":"Dong","year":"2020","journal-title":"Front. Comput. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1023\/A:1007607513941","article-title":"An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization","volume":"40","author":"Dietterich","year":"2000","journal-title":"Mach. Learn."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1080\/02564602.2014.906859","article-title":"A Review of Ensemble Learning Based Feature Selection","volume":"31","author":"Guan","year":"2014","journal-title":"IETE Tech. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2018.11.008","article-title":"Ensembles for Feature Selection: A Review and Future Trends","volume":"52","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.eiar.2015.05.005","article-title":"Multivariate Pluvial Flood Damage Models","volume":"54","author":"Verhofstadt","year":"2015","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106397","DOI":"10.1016\/j.eiar.2020.106397","article-title":"A GIS-Based Spatial Multi-Index Model for Flood Risk Assessment in the Yangtze River Basin, China","volume":"83","author":"Zhang","year":"2020","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106794","DOI":"10.1016\/j.eiar.2022.106794","article-title":"Urban Watershed Ecosystem Health Assessment and Ecological Management Zoning Based on Landscape Pattern and SWMM Simulation: A Case Study of Yangmei River Basin","volume":"95","author":"Zhao","year":"2022","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0165-1684(94)90060-4","article-title":"Topographic Distance and Watershed Lines","volume":"38","author":"Meyer","year":"1994","journal-title":"Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5194\/hess-10-101-2006","article-title":"On the Calculation of the Topographic Wetness Index: Evaluation of Different Methods Based on Field Observations","volume":"10","author":"Zinko","year":"2006","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/02626667909491834","article-title":"A Physically Based, Variable Contributing Area Model of Basin Hydrology \/ Un Mod\u00e8le \u00e0 Base Physique de Zone d\u2019appel Variable de l\u2019hydrologie Du Bassin Versant","volume":"24","author":"BEVEN","year":"1979","journal-title":"Hydrol. Sci. Bull."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2158","DOI":"10.1111\/risa.12597","article-title":"The Impact of Perceived Flood Exposure on Flood-Risk Perception: The Role of Distance: Flood-Risk Perception: The Role of Distance","volume":"36","author":"Brereton","year":"2016","journal-title":"Risk Anal."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1133","DOI":"10.1016\/j.scitotenv.2017.10.037","article-title":"Mapping Flood Susceptibility in Mountainous Areas on a National Scale in China","volume":"615","author":"Zhao","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1130\/0016-7606(1945)56[275:EDOSAT]2.0.CO;2","article-title":"Erosional Development of Streams and Their Drainage Basins. Hydrophysical Approach To Quantitative Morphology","volume":"56","author":"Horton","year":"1945","journal-title":"GSA Bull."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1080\/01431160304987","article-title":"Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery","volume":"24","author":"Zha","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1080\/2150704X.2013.763297","article-title":"Improved NDBI Differencing Algorithm for Built-up Regions Change Detection from Remote-Sensing Data: An Automated Approach","volume":"4","author":"Varshney","year":"2013","journal-title":"Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106666","DOI":"10.1016\/j.eiar.2021.106666","article-title":"The Spatiotemporal Dynamics of Urbanisation and Local Climate: A Case Study of Islamabad, Pakistan","volume":"91","author":"Aslam","year":"2021","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"(2001). Breiman Random Forests. Mach. Learn., 45, 5\u201332.","DOI":"10.1023\/A:1010933404324"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"107093","DOI":"10.1016\/j.eiar.2023.107093","article-title":"Environmental Disaster and Public Rescue: A Social Media Perspective","volume":"100","author":"Li","year":"2023","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"107050","DOI":"10.1016\/j.eiar.2023.107050","article-title":"Imputing Environmental Impact Missing Data of the Industrial Sector for Chinese Cities: A Machine Learning Approach","volume":"100","author":"Chen","year":"2023","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_59","unstructured":"Hecht-Nielsen, R. (1992). Neural Networks for Perception, Elsevier."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1061\/(ASCE)1084-0699(2002)7:4(270)","article-title":"Mathematical Modeling of Watershed Hydrology","volume":"7","author":"Singh","year":"2002","journal-title":"J. Hydrol. Eng."},{"key":"ref_61","first-page":"1","article-title":"ROC Curve Estimation: An Overview","volume":"12","author":"Subtil","year":"2014","journal-title":"REVSTAT-Stat. J."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"47","DOI":"10.5194\/isprsarchives-XL-4-W3-47-2013","article-title":"Research on Geographical Environment Unit Division Based on the Method of Natural Breaks (Jenks)","volume":"XL-4\/W3","author":"Chen","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_63","first-page":"1","article-title":"A Coarse-Grid Approach to Representing Building Blockage Effects in 2D Urban Flood Modelling","volume":"426\u2013427","author":"Chen","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.advwatres.2012.02.012","article-title":"Building Treatments for Urban Flood Inundation Models and Implications for Predictive Skill and Modeling Efficiency","volume":"41","author":"Schubert","year":"2012","journal-title":"Adv. Water Resour."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mallick, R.B., Tao, M., and MK, N. (2018). Impact of Flooding on Roadways. Geotech. Nat. Eng. Sustain. Technol. GeoNEst, 385\u2013397.","DOI":"10.1007\/978-981-10-7721-0_23"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:45:40Z","timestamp":1760103940000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"references-count":65,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020320"],"URL":"https:\/\/doi.org\/10.3390\/rs16020320","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}