{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T01:08:14Z","timestamp":1783213694839,"version":"3.54.6"},"reference-count":105,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T00:00:00Z","timestamp":1620086400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014206","name":"National Key Laboratory Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014206","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41861134008"],"award-info":[{"award-number":["41861134008"]}],"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":["2019QZKK0902"],"award-info":[{"award-number":["2019QZKK0902"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC1505202"],"award-info":[{"award-number":["2018YFC1505202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Geoscience Frontiers"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1016\/j.gsf.2021.101224","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T02:32:45Z","timestamp":1620181965000},"page":"101224","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":176,"title":["Flooding and its relationship with land cover change, population growth, and road density"],"prefix":"10.1016","volume":"12","author":[{"given":"Mahfuzur","family":"Rahman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Ningsheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Golam Iftekhar","family":"Mahmud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Monirul","family":"Islam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid Reza","family":"Pourghasemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hilal","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jules Maurice","family":"Habumugisha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rana Muhammad Ali","family":"Washakh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehtab","family":"Alam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enlong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huayong","family":"Ni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Shufeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashraf","family":"Dewan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.gsf.2021.101224_b0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.landusepol.2020.104868","article-title":"The effects of changing land use and flood hazard on poverty in coastal Bangladesh","volume":"99","author":"Adnan","year":"2020","journal-title":"Land Use Policy"},{"key":"10.1016\/j.gsf.2021.101224_b0010","article-title":"Flash flood potential prioritization of sub-basins in an ungauged basin in Turkey using traditional multi-criteria decision-making methods","volume":"1\u201313","author":"Akay","year":"2020","journal-title":"Soft. Comput."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_bib571","first-page":"438","article-title":"Instance reduction for avoiding overfitting in decision trees","volume":"30","author":"Al-Akhras","year":"2021","journal-title":"J. Intell. Syst."},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_b0015","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1002\/2016EF000485","article-title":"Global projections of river flood risk in a warmer world","volume":"5","author":"Alfieri","year":"2017","journal-title":"Earth\u2019s Future"},{"issue":"9","key":"10.1016\/j.gsf.2021.101224_b0020","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","author":"Al-Abadi","year":"2018","journal-title":"Arabian J. Geosci."},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_b0025","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s11069-019-03821-y","article-title":"Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness","volume":"100","author":"Al-Abadi","year":"2020","journal-title":"Nat. Hazard."},{"key":"10.1016\/j.gsf.2021.101224_b0030","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s41748-019-00141-w","article-title":"Flood hazard, vulnerability and risk assessment for different land use classes using a flow model","volume":"4","author":"Al Baky","year":"2019","journal-title":"Earth Syst. Environ."},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_b0035","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1007\/s40808-019-00593-z","article-title":"Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India","volume":"5","author":"Ali","year":"2019","journal-title":"Model. Earth Syst. Environ."},{"key":"10.1016\/j.gsf.2021.101224_b0040","first-page":"131","article-title":"Modelling the flood-risk extent using LISFLOOD-FP in a complex watershed: case study of Mundeni Aru River Basin, Sri Lanka","volume":"370","author":"Amarnath","year":"2015","journal-title":"Proc. Int. Assoc. Hydrol. Sci."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_b0045","doi-asserted-by":"crossref","first-page":"1765703","DOI":"10.1080\/20964129.2020.1765703","article-title":"Flood risk assessment using the CV-TOPSIS method for the Belt and Road Initiative: an empirical study of Southeast Asia","volume":"6","author":"An","year":"2020","journal-title":"Ecosyst. Health Sustainability"},{"key":"10.1016\/j.gsf.2021.101224_b0050","article-title":"Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India","volume":"750","author":"Arora","year":"2020","journal-title":"Sci. Total Environ."},{"issue":"21","key":"10.1016\/j.gsf.2021.101224_b0055","doi-asserted-by":"crossref","first-page":"3568","DOI":"10.3390\/rs12213568","article-title":"Flash flood susceptibility modeling using new approaches of hybrid and ensemble tree-based machine learning algorithms","volume":"12","author":"Band","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.gsf.2021.101224_b0060","article-title":"Application of the soil conservation service model in small and medium basins of the mountainous region of Heilongjiang, China","author":"Bazai","year":"2021","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"10.1016\/j.gsf.2021.101224_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.earscirev.2020.103432","article-title":"Increasing glacial lake outburst flood hazard in response to surge glaciers in the Karakoram","volume":"212","author":"Bazai","year":"2021","journal-title":"Earth Sci. Rev."},{"key":"10.1016\/j.gsf.2021.101224_b0070","series-title":"Bangladesh Population and Housing Census-2011 (Zila Report: Sylhet), 2019","author":"BBS","year":"2011"},{"key":"10.1016\/j.gsf.2021.101224_b0075","series-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"10.1016\/j.gsf.2021.101224_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2020.112148","article-title":"High-resolution wall-to-wall land-cover mapping and land change assessment for Australia from 1985 to 2015","volume":"252","author":"Calder\u00f3n-Loor","year":"2021","journal-title":"Remote Sens. Environ."},{"issue":"1\u20132","key":"10.1016\/j.gsf.2021.101224_b0095","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/S0169-1368(02)00111-7","article-title":"Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines","volume":"22","author":"Carranza","year":"2003","journal-title":"Ore Geol. Rev."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_b0100","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41398-019-0607-2","article-title":"Recommendations and future directions for supervised machine learning in psychiatry","volume":"9","author":"Cearns","year":"2019","journal-title":"Transl. Psychiatry"},{"key":"10.1016\/j.gsf.2021.101224_b0120","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1016\/j.scitotenv.2018.10.064","article-title":"An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines","volume":"651","author":"Choubin","year":"2019","journal-title":"Sci. Total Environ."},{"issue":"8","key":"10.1016\/j.gsf.2021.101224_b0125","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1007\/s12665-018-7498-z","article-title":"Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches","volume":"77","author":"Choubin","year":"2018","journal-title":"Environ. Earth Sci."},{"issue":"5","key":"10.1016\/j.gsf.2021.101224_b0130","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1016\/j.asr.2019.12.003","article-title":"Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India","volume":"65","author":"Chowdhuri","year":"2020","journal-title":"Adv. Space Res."},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_b0135","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s11069-017-2947-7","article-title":"Use of remote sensing data in comprehending an extremely unusual flooding event over southwest Bangladesh","volume":"88","author":"Chowdhury","year":"2017","journal-title":"Nat. Hazard."},{"issue":"9","key":"10.1016\/j.gsf.2021.101224_b0165","doi-asserted-by":"crossref","first-page":"3607","DOI":"10.3390\/su12093607","article-title":"Spatiotemporal analysis of land cover changes in the Chemoga Basin, Ethiopia, using Landsat and Google Earth Images","volume":"12","author":"Damtea","year":"2020","journal-title":"Sustainability"},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_b0175","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/j.2517-6161.1968.tb00722.x","article-title":"A generalization of Bayesian inference","volume":"30","author":"Dempster","year":"1968","journal-title":"J. R. Stat Soc.: Ser. B"},{"key":"10.1016\/j.gsf.2021.101224_b0180","series-title":"Cartography: Thematic Map Design, Guide","author":"Dent","year":"1993"},{"issue":"9","key":"10.1016\/j.gsf.2021.101224_b0185","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1007\/s11269-006-9116-1","article-title":"Evaluating flood hazard for land-use planning in greater Dhaka of Bangladesh using remote sensing and GIS techniques","volume":"21","author":"Dewan","year":"2007","journal-title":"Water Resour. Manage."},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_b0190","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.apgeog.2008.12.005","article-title":"Land use and land cover change in greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization","volume":"29","author":"Dewan","year":"2009","journal-title":"Appl. Geogr."},{"key":"10.1016\/j.gsf.2021.101224_b0195","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.wace.2014.11.001","article-title":"Societal impacts and vulnerability to floods in Bangladesh and Nepal","volume":"7","author":"Dewan","year":"2015","journal-title":"Weather Clim. Extremes"},{"key":"10.1016\/j.gsf.2021.101224_b0200","first-page":"35","author":"Douven","year":"2009"},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_b0205","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/13658816.2020.1808897","article-title":"A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping","volume":"35","author":"Fang","year":"2020","journal-title":"Int. J. Geog. Inf. Sci."},{"key":"10.1016\/j.gsf.2021.101224_b0210","series-title":"Road Ecology: Science and Solutions","author":"Forman","year":"2003"},{"key":"10.1016\/j.gsf.2021.101224_b0215","series-title":"Geographically Weighted Regression: The Analysis of Spatially Varying Relationships","author":"Fotheringham","year":"2003"},{"key":"10.1016\/j.gsf.2021.101224_b0220","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.isprsjprs.2020.07.013","article-title":"Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples","volume":"167","author":"Ghorbanian","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.gsf.2021.101224_b0225","unstructured":"Gouravaraju, S., Narayan, J., Sauer, R.A., Gautam, S.S., 2020. A Bayesian regularization-backpropagation neural network model for peeling computations. arXiv preprint arXiv. 2006.16409."},{"issue":"6","key":"10.1016\/j.gsf.2021.101224_b0230","first-page":"0511","article-title":"Predictions of future hydrological conditions and contribution of snow and ice melt in total discharge of Shigar River Basin in Central Karakoram, Pakistan","volume":"9","author":"Hassan","year":"2017","journal-title":"Sci. Cold Arid Reg."},{"key":"10.1016\/j.gsf.2021.101224_b0235","article-title":"Rock glacier inventory, permafrost probability distribution modeling and associated hazards in the Hunza River Basin, Western Karakoram, Pakistan","volume":"146833","author":"Hassan","year":"2021","journal-title":"Sci. Total Environ."},{"issue":"8","key":"10.1016\/j.gsf.2021.101224_b0240","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1007\/s11629-019-5409-8","article-title":"Assessing the performance of decision tree and neural network models in mapping soil properties","volume":"16","author":"Hateffard","year":"2019","journal-title":"J. Mountain Sci."},{"key":"10.1016\/j.gsf.2021.101224_b0245","series-title":"Local Level Flood Forecasting System Using Mathematical Model Incorporating WRF Model Predicted Rainfall.","author":"Hossain","year":"2015"},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_b0250","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s10661-019-7256-z","article-title":"Impacts of climatic variability on agriculture and options for adaptation in the Surma River basin, Bangladesh","volume":"191","author":"Hossain","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"10.1016\/j.gsf.2021.101224_b0255","article-title":"Flood susceptibility modelling using advanced ensemble machine learning models","author":"Islam","year":"2020","journal-title":"Geosci. Front."},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_b0260","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1080\/02626660009492334","article-title":"Development of flood hazard maps of Bangladesh using NOAA-AVHRR images with GIS","volume":"45","author":"Islam","year":"2000","journal-title":"Hydrol. Sci. J."},{"key":"10.1016\/j.gsf.2021.101224_b0265","doi-asserted-by":"crossref","DOI":"10.1111\/jfr3.12533","article-title":"Urban flood risk mapping using an optimised additive weighting methodology based on open data","volume":"12","author":"Jato-Espino","year":"2019","journal-title":"J. Flood Risk Manage."},{"key":"10.1016\/j.gsf.2021.101224_b0270","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.trd.2014.07.010","article-title":"Spatial and temporal distribution of expressway and its relationships to land cover and population: a case study of Beijing, China","volume":"32","author":"Ji","year":"2014","journal-title":"Transp. Res. Part D: Transp. Environ."},{"issue":"10","key":"10.1016\/j.gsf.2021.101224_b0275","first-page":"1946","article-title":"Flood susceptibility appraisal in Ponnaiyar River Basin, India using frequency ratio (FR) and Shannon\u2019s Entropy (SE) models","volume":"5","author":"Jothibasu","year":"2016","journal-title":"Int. J. Adv. Rem. Sens. GIS"},{"key":"10.1016\/j.gsf.2021.101224_b0280","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.jenvman.2013.11.032","article-title":"A method for mapping flood hazard along roads","volume":"133","author":"Kalantari","year":"2014","journal-title":"J. Environ. Manage."},{"key":"10.1016\/j.gsf.2021.101224_b0285","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.ijdrr.2018.06.011","article-title":"Resilience to flash floods in wetland communities of northeastern Bangladesh","volume":"31","author":"Kamal","year":"2018","journal-title":"Int. J. Disaster Risk Reduct."},{"issue":"259","key":"10.1016\/j.gsf.2021.101224_bib573","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1017\/jog.2020.51","article-title":"Future projection of cryospheric and hydrologic regimes in Koshi River basin, Central Himalaya, using coupled glacier dynamics and glacio-hydrological models","volume":"66","author":"Khadka","year":"2020","journal-title":"J. Glaciol."},{"issue":"8","key":"10.1016\/j.gsf.2021.101224_b0300","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.3390\/w11081654","article-title":"Flood risk assessment of global watersheds based on multiple machine learning models","volume":"11","author":"Li","year":"2019","journal-title":"Water"},{"issue":"9","key":"10.1016\/j.gsf.2021.101224_b0305","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1007\/s11269-019-02293-w","article-title":"Comparison between different distributed methods for flood susceptibility mapping","volume":"33","author":"Liuzzo","year":"2019","journal-title":"Water Resour. Manage."},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_b0310","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","article-title":"Bayesian interpolation","volume":"4","author":"MacKay","year":"1992","journal-title":"Neural Comput."},{"key":"10.1016\/j.gsf.2021.101224_b0315","article-title":"Prediction of highly flood prone areas by GIS based heuristic and statistical model in a monsoon dominated region of Bengal Basin","volume":"19","author":"Malik","year":"2020","journal-title":"Remote Sens. Appl.: Soc. Environ."},{"key":"10.1016\/j.gsf.2021.101224_b0320","article-title":"Application of 2D numerical simulation for rating curve development and inundation area mapping: a case study of monsoon dominated Dwarkeswar River","volume":"1\u201311","author":"Malik","year":"2020","journal-title":"Int. J. River Basin Manage."},{"key":"10.1016\/j.gsf.2021.101224_b0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.uclim.2020.100599","article-title":"Trend of extreme rainfall events using suitable Global Circulation Model to combat the water logging condition in Kolkata Metropolitan Area","volume":"32","author":"Malik","year":"2020","journal-title":"Urban Clim."},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_b0330","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s11069-011-0060-x","article-title":"Assessment of flood hazard, vulnerability and risk of mid-eastern Dhaka using DEM and 1D hydrodynamic model","volume":"61","author":"Masood","year":"2012","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.gsf.2021.101224_b0335","article-title":"The importance of the model choice for experimental semivariogram modeling and its consequence in evaluation process","volume":"2013","author":"Mazzella","year":"2013","journal-title":"J. Eng."},{"key":"10.1016\/j.gsf.2021.101224_b0340","series-title":"Geoinformatics and Atmospheric Science. Pageoph Topical Volumes","first-page":"221","article-title":"The use of geospatial technologies in flood hazard mapping and assessment: case study from River Evros","author":"Mentzafou","year":"2018"},{"key":"10.1016\/j.gsf.2021.101224_b0345","article-title":"Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance","volume":"103225","author":"Merghadi","year":"2020","journal-title":"Earth Sci. Rev."},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_bib566","first-page":"77","article-title":"MCDM approach for mitigation of flooding risks in Odisha (India) based on information retrieval","volume":"14","author":"Mishra","year":"2020","journal-title":"Int. J. Cognit. Inf. Nat. Intell."},{"issue":"2","key":"10.1016\/j.gsf.2021.101224_b0350","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1080\/19475705.2017.1294113","article-title":"Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS","volume":"8","author":"Mojaddadi","year":"2017","journal-title":"Geomatics Nat. Hazard. Risk"},{"issue":"\u00bd","key":"10.1016\/j.gsf.2021.101224_b0355","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on continuous stochastic phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"10.1016\/j.gsf.2021.101224_b0360","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10708-019-09984-2","article-title":"Detecting flood prone areas in Harris County: a GIS based analysis","volume":"85","author":"Mukherjee","year":"2020","journal-title":"GeoJournal"},{"issue":"20","key":"10.1016\/j.gsf.2021.101224_b0365","doi-asserted-by":"crossref","first-page":"3301","DOI":"10.3390\/rs12203301","article-title":"A hybrid data balancing method for classification of imbalanced training data within Google Earth Engine: case studies from Mountainous Regions","volume":"12","author":"Naboureh","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.gsf.2021.101224_b0370","series-title":"Intense Flooding in Bangladesh, 2020","author":"NASA","year":"2020"},{"issue":"9","key":"10.1016\/j.gsf.2021.101224_b0375","doi-asserted-by":"crossref","first-page":"1373","DOI":"10.3390\/rs12091373","article-title":"A new modeling approach for spatial prediction of flash flood with biogeography optimized CHAID tree ensemble and remote sensing data","volume":"12","author":"Nguyen","year":"2020","journal-title":"Remote Sens."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_b0380","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1515\/jwld-2017-0080","article-title":"Selection of a semivariogram model in the study of spatial distribution of soil moisture","volume":"35","author":"Obro\u015blak","year":"2017","journal-title":"J. Water Land Dev."},{"key":"10.1016\/j.gsf.2021.101224_b0385","series-title":"Geomorphic Signatures of Active Tectonics from Sylhet City and Adjoining Areas Surma Basin","first-page":"108","author":"Ovi","year":"2015"},{"key":"10.1016\/j.gsf.2021.101224_b0390","series-title":"Habitat, Ecology and Ekistics Case Studies of Human-Environment Interactions in India","first-page":"225","article-title":"Flood frequency analysis and its management in selected part of Bardhaman district, West Bengal","author":"Pal","year":"2020"},{"issue":"5","key":"10.1016\/j.gsf.2021.101224_b0395","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1007\/s42452-019-0422-7","article-title":"GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India","volume":"1","author":"Pal","year":"2019","journal-title":"SN Appl. Sci."},{"issue":"7","key":"10.1016\/j.gsf.2021.101224_bib569","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.5194\/hess-13-1019-2009","article-title":"A look at the links between drainage density and flood statistics","volume":"13","author":"Pallard","year":"2009","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.gsf.2021.101224_b0400","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.gsf.2021.101224_b0405","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1007\/s41976-019-00018-6","article-title":"Application of the GIS-based probabilistic models for mapping the flood susceptibility in Bansloi sub-basin of Ganga-Bhagirathi river and their comparison","volume":"2","author":"Paul","year":"2019","journal-title":"Remote Sens. Earth Syst. Sci."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_b0410","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s11600-018-0233-z","article-title":"Flood risk assessment and mapping using AHP in arid and semiarid regions","volume":"67","author":"Radwan","year":"2019","journal-title":"Acta Geophys."},{"key":"10.1016\/j.gsf.2021.101224_b0415","article-title":"Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India","volume":"1\u201326","author":"Ramesh","year":"2020","journal-title":"Geocarto Int."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_b0420","first-page":"111","article-title":"Changes in land use\/cover using geospatial techniques: a case study of Ramnagar town area, district Nainital, Uttarakhand, India, Egypt","volume":"16","author":"Rawat","year":"2013","journal-title":"J. Remote Sens. Space. Sci."},{"key":"10.1016\/j.gsf.2021.101224_b0425","series-title":"Geology of Bangladesh","author":"Reimann","year":"1993"},{"key":"10.1016\/j.gsf.2021.101224_b0430","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2020.122757","article-title":"Threats of climate and land use change on future flood susceptibility","volume":"272","author":"Roy","year":"2020","journal-title":"J. Cleaner Prod."},{"issue":"9","key":"10.1016\/j.gsf.2021.101224_b0435","doi-asserted-by":"crossref","first-page":"3092","DOI":"10.1002\/ldr.3058","article-title":"Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process: application of the fuzzy decision making trial and evaluation laboratory approach","volume":"29","author":"Sajedi-Hosseini","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"10.1016\/j.gsf.2021.101224_bib567","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.scitotenv.2019.02.328","article-title":"A flood susceptibility model at the national scale based on multicriteria analysis","volume":"667","author":"Santos","year":"2019","journal-title":"Sci. Total Environ."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_b0440","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s13201-019-1102-x","article-title":"Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region","volume":"10","author":"Sarkar","year":"2020","journal-title":"Appl. Water Sci."},{"issue":"19","key":"10.1016\/j.gsf.2021.101224_b0445","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.3390\/rs11192331","article-title":"Flood mapping with convolutional neural networks using spatio-contextual pixel information","volume":"11","author":"Sarker","year":"2019","journal-title":"Remote Sens."},{"issue":"5","key":"10.1016\/j.gsf.2021.101224_b0460","first-page":"67","article-title":"Understanding the permutation of integrated agriculture approach for bringing resilience at Haor Basins in Bangladesh","volume":"5","author":"Sharif","year":"2017","journal-title":"Int. J. Agric. For. Fish."},{"issue":"7","key":"10.1016\/j.gsf.2021.101224_bib570","doi-asserted-by":"crossref","first-page":"6050","DOI":"10.1002\/2017WR020784","article-title":"Tree\u2010based flood damage modeling of companies: Damage processes and model performance","volume":"53","author":"Sieg","year":"2017","journal-title":"Water Resour. Res."},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_bib572","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s10533-018-0449-7","article-title":"The impact of flooding on aquatic ecosystem services","volume":"141","author":"Talbot","year":"2018","journal-title":"Biogeochemistry"},{"key":"10.1016\/j.gsf.2021.101224_b0465","article-title":"Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms","volume":"1\u201324","author":"Talukdar","year":"2020","journal-title":"Stoch. Environ. Res. Risk Assess."},{"issue":"13","key":"10.1016\/j.gsf.2021.101224_b0470","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s12665-018-7667-0","article-title":"The application of a Dempster\u2013Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods","volume":"77","author":"Tehrany","year":"2018","journal-title":"Environ. Earth Sci."},{"key":"10.1016\/j.gsf.2021.101224_b0475","article-title":"A novel GIS-based ensemble technique for flood susceptibility mapping using evidential belief function and support vector machine: Brisbane, Australia","volume":"7","author":"Tehrany","year":"2019","journal-title":"PeerJ"},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_b0480","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s10661-019-7327-1","article-title":"Flood hazard mapping using geospatial techniques and satellite images\u2014a case study of coastal district of Tamil Nadu","volume":"191","author":"Thirumurugan","year":"2019","journal-title":"Environ. Monit. Assess."},{"issue":"10","key":"10.1016\/j.gsf.2021.101224_b0490","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1002\/hyp.5666","article-title":"Flood hazard and risk analysis in the southwest region of Bangladesh","volume":"19","author":"Tingsanchali","year":"2005","journal-title":"Hydrol. Process."},{"issue":"3","key":"10.1016\/j.gsf.2021.101224_b0495","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0229153","article-title":"GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River Basin, eastern Hindu Kush, Pakistan","volume":"15","author":"Ullah","year":"2020","journal-title":"PLoS One"},{"issue":"9","key":"10.1016\/j.gsf.2021.101224_bib568","doi-asserted-by":"crossref","first-page":"338","DOI":"10.3390\/geosciences10090338","article-title":"Application of nonhydraulic delineation method of flood hazard areas using LiDAR-based data","volume":"10","author":"Ureta","year":"2020","journal-title":"Geosciences"},{"key":"10.1016\/j.gsf.2021.101224_b0500","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.sbspro.2013.12.027","article-title":"A study on multiple linear regression analysis","volume":"106","author":"Uyan\u0131k","year":"2013","journal-title":"Proc. Soc. Behav. Sci."},{"issue":"7","key":"10.1016\/j.gsf.2021.101224_b0505","doi-asserted-by":"crossref","first-page":"5322","DOI":"10.1002\/2016WR019036","article-title":"Attribution of regional flood changes based on scaling fingerprints","volume":"52","author":"Viglione","year":"2016","journal-title":"Water Resour. Res."},{"key":"10.1016\/j.gsf.2021.101224_b0510","series-title":"The Human Cost of Weather-related Disasters 1995\u20132015","author":"Wahlstrom","year":"2015"},{"issue":"13","key":"10.1016\/j.gsf.2021.101224_b0515","doi-asserted-by":"crossref","first-page":"3465","DOI":"10.1007\/s11269-011-9866-2","article-title":"A GIS-based spatial multi-criteria approach for flood risk assessment in the Dongting Lake Region, Hunan, Central China","volume":"25","author":"Wang","year":"2011","journal-title":"Water Resour. Manage."},{"key":"10.1016\/j.gsf.2021.101224_b0520","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2019.124482","article-title":"Flood susceptibility mapping using convolutional neural network frameworks","volume":"582","author":"Wang","year":"2020","journal-title":"J. Hydrol."},{"key":"10.1016\/j.gsf.2021.101224_b0525","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/j.scitotenv.2018.07.353","article-title":"Development of a spatially complete floodplain map of the conterminous United States using random forest","volume":"647","author":"Woznicki","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.gsf.2021.101224_b0530","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.advengsoft.2017.09.004","article-title":"Predicting compressive strength of lightweight foamed concrete using extreme learning machine model","volume":"115","author":"Yaseen","year":"2018","journal-title":"Adv. Eng. Softw."},{"issue":"3\u20134","key":"10.1016\/j.gsf.2021.101224_b0535","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.enggeo.2005.02.002","article-title":"Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey)","volume":"79","author":"Yesilnacar","year":"2005","journal-title":"Eng. Geol."},{"key":"10.1016\/j.gsf.2021.101224_b0540","doi-asserted-by":"crossref","DOI":"10.1016\/j.catena.2020.104851","article-title":"Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: a case study in Jiuzhaigou region","volume":"195","author":"Yi","year":"2020","journal-title":"Catena"},{"key":"10.1016\/j.gsf.2021.101224_b0545","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.jhydrol.2016.03.037","article-title":"Evaluating the impact and risk of pluvial flash flood on intra-urban road network: a case study in the city center of Shanghai, China","volume":"537","author":"Yin","year":"2016","journal-title":"J. Hydrol."},{"issue":"1","key":"10.1016\/j.gsf.2021.101224_b0550","doi-asserted-by":"crossref","first-page":"183","DOI":"10.5194\/hess-21-183-2017","article-title":"Effects of land use\/land cover and climate changes on surface runoff in a semi-humid and semi-arid transition zone in northwest China","volume":"21","author":"Yin","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"10.1016\/j.gsf.2021.101224_b0555","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"issue":"14","key":"10.1016\/j.gsf.2021.101224_b0560","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.3390\/rs12142180","article-title":"Optimization of computational intelligence models for landslide susceptibility evaluation","volume":"12","author":"Zhao","year":"2020","journal-title":"Remote Sens."},{"key":"10.1016\/j.gsf.2021.101224_b0565","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.catena.2016.06.009","article-title":"Impacts of land use\u2013land cover change and urbanization on flooding: a case study of Oshiwara River Basin in Mumbai, India","volume":"145","author":"Zope","year":"2016","journal-title":"Catena"}],"container-title":["Geoscience Frontiers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1674987121000888?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1674987121000888?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:50:30Z","timestamp":1778622630000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1674987121000888"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11]]},"references-count":105,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["S1674987121000888"],"URL":"https:\/\/doi.org\/10.1016\/j.gsf.2021.101224","relation":{},"ISSN":["1674-9871"],"issn-type":[{"value":"1674-9871","type":"print"}],"subject":[],"published":{"date-parts":[[2021,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Flooding and its relationship with land cover change, population growth, and road density","name":"articletitle","label":"Article Title"},{"value":"Geoscience Frontiers","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.gsf.2021.101224","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 China University of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"101224"}}