{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T02:44:36Z","timestamp":1782441876398,"version":"3.54.5"},"reference-count":93,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T00:00:00Z","timestamp":1709164800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use\/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments.<\/jats:p>","DOI":"10.3390\/rs16050858","type":"journal-article","created":{"date-parts":[[2024,2,29]],"date-time":"2024-02-29T08:13:44Z","timestamp":1709194424000},"page":"858","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Flood Susceptibility Mapping Using SAR Data and Machine Learning Algorithms in a Small Watershed in Northwestern Morocco"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7721-7759","authenticated-orcid":false,"given":"Sliman","family":"Hitouri","sequence":"first","affiliation":[{"name":"Geosciences Laboratory, Department of Geology, Faculty of Sciences, University Ibn Tofail, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0019-6862","authenticated-orcid":false,"given":"Meriame","family":"Mohajane","sequence":"additional","affiliation":[{"name":"Construction Technologies Institute, National Research Council (CNR), Polo Tecnologico di San Giovanni a Teduccio, 80146 Napoli, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meriam","family":"Lahsaini","sequence":"additional","affiliation":[{"name":"Institute of Geosciences and Earth Resources (IGG), National Research Council (CNR), Via Moruzzi 1, 56126 Pisa, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7488-5591","authenticated-orcid":false,"given":"Sk Ajim","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Science, Aligarh Muslim University (AMU), Aligarh 202002, Uttar Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8900-6588","authenticated-orcid":false,"given":"Tadesual Asamin","family":"Setargie","sequence":"additional","affiliation":[{"name":"Faculty of Civil and Water Resources Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar P.O. Box 26, Ethiopia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1114-7660","authenticated-orcid":false,"given":"Gaurav","family":"Tripathi","sequence":"additional","affiliation":[{"name":"Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4865-0681","authenticated-orcid":false,"given":"Paola","family":"D\u2019Antonio","sequence":"additional","affiliation":[{"name":"School of Agricultural, Forestry, Environmental and Food Sciences, University of Basilicata, 85100 Potenza, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9420-2804","authenticated-orcid":false,"given":"Suraj Kumar","family":"Singh","sequence":"additional","affiliation":[{"name":"Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur 302017, Rajasthan, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4173-5241","authenticated-orcid":false,"given":"Antonietta","family":"Varasano","sequence":"additional","affiliation":[{"name":"ITC-CNR, Construction Technologies Institute, National Research Council (CNR), 70124 Bari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135983","DOI":"10.1016\/j.scitotenv.2019.135983","article-title":"Integrated machine learning methods with resampling algorithms for flood susceptibility prediction","volume":"705","author":"Dodangeh","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1007\/s11069-011-9869-6","article-title":"The Indus flood of 2010 in Pakistan: A perspective analysis using remote sensing data","volume":"59","author":"Gaurav","year":"2011","journal-title":"Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1175\/JHM-D-12-0155.1","article-title":"Simulation of a flash flooding storm at the steep edge of the Himalayas","volume":"15","author":"Kumar","year":"2014","journal-title":"J. Hydrometeorol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.yqres.2005.12.001","article-title":"Oxygen isotope composition of annually banded modern and mid-Holocene travertine and evidence of paleomonsoon floods, Grand Canyon, Arizona, USA","volume":"65","author":"Kaufman","year":"2006","journal-title":"Quat. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.ejrh.2015.02.003","article-title":"The character and causes of flash flood occurrence changes in mountainous small basins of Southern California under projected climatic change","volume":"3","author":"Modrick","year":"2015","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_6","first-page":"207","article-title":"Flood detection and flood mapping using multi-temporal synthetic aperture radar and optical data","volume":"23","author":"Anusha","year":"2020","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.5194\/hess-21-1279-2017","article-title":"Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model","volume":"21","author":"Li","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hosseinpoor, H., and Samadzadegan, F. (2020, January 18\u201320). Convolutional neural network for building extraction from high-resolution remote sensing images. Proceedings of the 2020 International Conference on Machine Vision and Image Processing (MVIP), Qom, Iran.","DOI":"10.1109\/MVIP49855.2020.9187483"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s11069-010-9525-6","article-title":"Waterlogging and flood hazards vulnerability and risk assessment in Indo Gangetic plain","volume":"55","author":"Pandey","year":"2010","journal-title":"Nat. Hazards"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40677-017-0069-x","article-title":"Consequences of Koshi flood 2008 in terms of sedimentation characteristics and agricultural practices","volume":"4","author":"Kafle","year":"2017","journal-title":"Geoenviron. Disasters"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2007","DOI":"10.1007\/s11069-020-04387-w","article-title":"Flood risk mapping and crop-water loss modeling using water footprint analysis in agricultural watershed, northern Iran","volume":"105","author":"Mohammadi","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Phan, A., Ha, D.N., D. Man, C., T. Nguyen, T., Q. Bui, H., and Nguyen, T.T.N. (2019). Rapid assessment of flood inundation and damaged rice area in red river delta from sentinel 1A imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172034"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.envsoft.2017.01.006","article-title":"Flood inundation modelling: A review of methods, recent advances and uncertainty analysis","volume":"90","author":"Teng","year":"2017","journal-title":"Environ. Model. Softw."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.jhydrol.2017.11.036","article-title":"Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model","volume":"556","author":"Afshari","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.envsoft.2018.11.005","article-title":"Moving to 3-D flood hazard maps for enhancing risk communication","volume":"111","author":"Macchione","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s12559-023-10179-8","article-title":"Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence","volume":"16","author":"Hassija","year":"2023","journal-title":"Cogn. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1111\/1467-9671.00093","article-title":"Using Synthetic Aperture Radar Imagery for Flood Modelling","volume":"6","author":"Galy","year":"2002","journal-title":"Trans. GIS"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"140596","DOI":"10.1016\/j.scitotenv.2020.140596","article-title":"Future projections of flood dynamics in the Vietnamese Mekong Delta","volume":"742","author":"Triet","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Penki, R., Basina, S., and Tanniru, S. (2022). Application of Geographical Information System-Based Analytical Hierarchy Process Modeling for Flood Susceptibility Mapping of Krishna District in Andhra Pradesh. Res. Square.","DOI":"10.21203\/rs.3.rs-1399020\/v1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1007\/s12524-020-01155-y","article-title":"Flood mapping using relevance vector machine and SAR data: A case study from Aqqala, Iran","volume":"48","author":"Sharifi","year":"2020","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101075","DOI":"10.1016\/j.gsf.2020.09.006","article-title":"Flood susceptibility modelling using advanced ensemble machine learning models","volume":"12","author":"Islam","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_22","unstructured":"(2024, January 08). Ministry of Equipment, Transport, Logistics and Water, \u00ab\u0648\u0632\u0627\u0631\u0629 \u0627\u0644\u062a\u062c\u0647\u064a\u0632 \u0648\u0627\u0644\u0645\u0627\u0621.\u00bb, Available online: https:\/\/www.equipement.gov.ma\/AR\/Pages\/Accueil.aspx."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"7763","DOI":"10.1016\/j.matpr.2021.03.484","article-title":"Risk of flooding of the national road N \u00b06 at the right of crossing the wadi Asla in the region of Taourirt","volume":"45","author":"Toufik","year":"2021","journal-title":"Mater. Today Proc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fadl, M.E., Jalhoum, M.E.M., AbdelRahman, M.A.E., Ali, E.A., Zahra, W.R., Abuzaid, A.S., Fiorentino, C., D\u2019Antonio, P., Belal, A.A., and Scopa, A. (2023). Soil Salinity Assessing and Mapping Using Several Statistical and Distribution Techniques in Arid and Semi-Arid Ecosystems, Egypt. Agronomy, 13.","DOI":"10.3390\/agronomy13020583"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1007\/s11069-012-0180-y","article-title":"Flood loss analysis and quantitative risk assessment in China","volume":"63","author":"Li","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.ecolind.2011.06.022","article-title":"Flood regulating ecosystem services\u2014Mapping supply and demand, in the Etropole municipality, Bulgaria","volume":"21","author":"Nedkov","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.enggeo.2009.12.006","article-title":"Urban flood hazard zoning in Tucum\u00e1n Province, Argentina, using GIS and multicriteria decision analysis","volume":"111","author":"Lutz","year":"2010","journal-title":"Eng. Geol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4271376","DOI":"10.1155\/2020\/4271376","article-title":"Flood detection and susceptibility mapping using sentinel-1 time series, alternating decision trees, and bag-adtree models","volume":"2020","author":"Mohammadi","year":"2020","journal-title":"Complexity"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1038\/s41598-020-69233-2","article-title":"A machine learning framework for multi-hazards modeling and mapping in a mountainous area","volume":"10","author":"Yousefi","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cardenas, M.B., Wilson, J.L., and Zlotnik, V.A. (2004). Impact of heterogeneity, bed forms, and stream curvature on subchannel hyporheic exchange. Water Resour. Res., 40.","DOI":"10.1029\/2004WR003008"},{"key":"ref_31","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. Manag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.gsf.2020.06.013","article-title":"Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran","volume":"12","author":"Ngo","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/nature02002","article-title":"Episodic sediment accumulation on Amazonian flood plains influenced by El Nino\/Southern Oscillation","volume":"425","author":"Aalto","year":"2003","journal-title":"Nature"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jaridenv.2011.11.025","article-title":"Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river","volume":"79","author":"Glenn","year":"2012","journal-title":"J. Arid Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/j.1365-2745.2007.01329.x","article-title":"Landscape configuration and flood frequency influence invasive shrubs in floodplain forests of the Wisconsin River (USA)","volume":"96","author":"Predick","year":"2008","journal-title":"J. Ecol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1016\/j.scitotenv.2017.10.114","article-title":"Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution","volume":"621","author":"Hong","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.jhydrol.2005.06.013","article-title":"RCM rainfall for UK flood frequency estimation. II. Climate change results","volume":"318","author":"Kay","year":"2006","journal-title":"J. Hydrol."},{"key":"ref_38","unstructured":"Khosravi, K., Melesse, A.M., Shahabi, H., Shirzadi, A., Chapi, K., and Hong, H. (2019). Extreme Hydrology and Climate Variability, Elsevier."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., Melesse, A.M., Thai Pham, B., Pourghasemi, H.R., Mahmoudi, M., and Bahrami, S. (2019). Flood spatial modeling in northern Iran using remote sensing and gis: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sens., 11.","DOI":"10.3390\/rs11131589"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Elsharkawy, M.M., Sheta, A.E.A.S., D\u2019Antonio, P., Abdelwahed, M.S., and Scopa, A. (2022). Tool for the Establishment of Agro-Management Zones Using GIS Techniques for Precision Farming in Egypt. Sustainability, 14.","DOI":"10.3390\/su14095437"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10064-017-1010-y","article-title":"A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China","volume":"77","author":"Chen","year":"2018","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1016\/j.jenvman.2019.06.102","article-title":"Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm","volume":"247","author":"Wang","year":"2019","journal-title":"J. Environ. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mohajane, M., Essahlaoui, A.L.I., Oudija, F., Hafyani, M.E., Hmaidi, A.E., Ouali, A.E., Randazzo, G., and Teodoro, A.C. (2018). Land use\/land cover (LULC) using landsat data series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5.","DOI":"10.3390\/environments5120131"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.jhydrol.2008.04.013","article-title":"Flood generation and sediment transport in experimental catchments affected by land use changes in the central Pyrenees","volume":"356","author":"Alvera","year":"2008","journal-title":"J. Hydrol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.geomorph.2007.02.026","article-title":"Geomorphic impacts of a 100-year flood: Kiwitea Stream, Manawatu catchment, New Zealand","volume":"98","author":"Fuller","year":"2008","journal-title":"Geomorphology"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Coppola, A., Di Renzo, G.C., Altieri, G., and D\u2019Antonio, P. (2020). Innovative Biosystems Engineering for Sustainable Agriculture, Forestry and Food Production, Springer International Publishing.","DOI":"10.1007\/978-3-030-39299-4"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hitouri, S., Varasano, A., Mohajane, M., Ijlil, S., Essahlaoui, N., Ali, S.A., Essahlaoui, A., Pham, Q.B., Waleed, M., and Palateerdham, S.K. (2022). Hybrid machine learning approach for gully erosion mapping susceptibility at a watershed scale. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11070401"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital terrain modelling: A review of hydrological, geomorphological, and biological applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1002\/esp.1723","article-title":"Longitudinal distributions of river flood power: The combined automated flood, elevation and stream power (CAFES) methodology","volume":"34","author":"Barker","year":"2009","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_50","first-page":"2587","article-title":"Detection of urban irregular development and green space destruction using normalized difference vegetation index (NDVI), principal component analysis (PCA) and post classification methods: A case study of Saqqez city","volume":"7","author":"Shahabi","year":"2012","journal-title":"Int. J. Phys. Sci."},{"key":"ref_51","first-page":"100599","article-title":"Performance evaluation of machine learning algorithms using optical and microwave data for LULC classification","volume":"23","author":"Chachondhia","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_52","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_53","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."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1080\/01431161.2020.1857877","article-title":"Parametric study of convolutional neural network based remote sensing image classification","volume":"42","author":"Shakya","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"107869","DOI":"10.1016\/j.ecolind.2021.107869","article-title":"Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area","volume":"129","author":"Mohajane","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1080\/19479830903561985","article-title":"Multi-sensor image fusion for pansharpening in remote sensing","volume":"1","author":"Ehlers","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_59","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 Topl\u2019a basin, Slovakia","volume":"117","author":"Ali","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.advwatres.2019.05.020","article-title":"Flood risk and its reduction in China","volume":"130","author":"Kundzewicz","year":"2019","journal-title":"Adv. Water Resour."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3527","DOI":"10.1038\/s41467-022-30727-4","article-title":"Flood exposure and poverty in 188 countries","volume":"13","author":"Rentschler","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"100212","DOI":"10.1016\/j.wace.2019.100212","article-title":"Increased flood risk in Indian sub-continent under the warming climate","volume":"25","author":"Ali","year":"2019","journal-title":"Weather Clim. Extrem."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"108555","DOI":"10.1016\/j.ress.2022.108555","article-title":"Critical facility accessibility rapid failure early-warning detection and redundancy mapping in urban flooding","volume":"224","author":"Gangwal","year":"2022","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zgurovsky, M., Yefremov, K., Gapon, S., and Pyshnograiev, I. (2022, January 4\u20137). Modeling of Potential Flooding Zones with Geomatics Tools. Proceedings of the 2022 IEEE 3rd International Conference on System Analysis & Intelligent Computing (SAIC), Kyiv, Ukraine.","DOI":"10.1109\/SAIC57818.2022.9923016"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hultquist, C., and Cervone, G. (2020). Integration of crowdsourced images, USGS networks, remote sensing, and a model to assess flood depth during hurricane florence. Remote Sens., 12.","DOI":"10.3390\/rs12050834"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Ekpetere, K., Abdelkader, M., Ishaya, S., Makwe, E., and Ekpetere, P. (2023). Integrating Satellite Imagery and Ground-Based Measurements with a Machine Learning Model for Monitoring Lake Dynamics over a Semi-Arid Region. Hydrology, 10.","DOI":"10.3390\/hydrology10040078"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e12647","DOI":"10.1111\/jfr3.12647","article-title":"Enhanced flood mapping using synthetic aperture radar (SAR) images, hydraulic modelling, and social media: A case study of Hurricane Harvey (Houston, TX)","volume":"13","author":"Scotti","year":"2020","journal-title":"J. Flood Risk Manag."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kumar, V., Azamathulla, H.M., Sharma, K.V., Mehta, D.J., and Maharaj, K.T. (2023). The state of the art in deep learning applications, challenges, and future prospects: A comprehensive review of flood forecasting and management. Sustainability, 15.","DOI":"10.3390\/su151310543"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Kumar, V., Sharma, K.V., Caloiero, T., Mehta, D.J., and Singh, K. (2023). Comprehensive overview of flood modeling approaches: A review of recent advances. Hydrology, 10.","DOI":"10.3390\/hydrology10070141"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"162066","DOI":"10.1016\/j.scitotenv.2023.162066","article-title":"Enhancing flood susceptibility modeling using multi-temporal SAR images, CHIRPS data, and hybrid machine learning algorithms","volume":"871","author":"Riazi","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_71","first-page":"238","article-title":"Towards Time Series Sensor Data to Accurately Map Flood Hazard and Assess Damages under Climate Change Using Google Earth Engine Cloud Platform and GIS\u2013Case of the Cities of Tetouan and Casablanca (Morocco)","volume":"5","author":"Wahbi","year":"2023","journal-title":"Ecol Eng."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ivan Ulloa, N., Chiang, S.-H., and Yun, S.-H. (2020). Flood proxy mapping with normalized difference sigma-naught index and Shannon\u2019s entropy. Remote Sens., 12.","DOI":"10.3390\/rs12091384"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1007\/s10661-019-7903-4","article-title":"Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin","volume":"191","author":"Agnihotri","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.5194\/hess-26-4345-2022","article-title":"Deep learning methods for flood mapping: A review of existing applications and future research directions","volume":"26","author":"Bentivoglio","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.catena.2018.12.011","article-title":"Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques","volume":"175","author":"Tehrany","year":"2019","journal-title":"Catena"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"13638","DOI":"10.1080\/10106049.2022.2082550","article-title":"Enhanced classification and regression tree (CART) by genetic algorithm (GA) and grid search (GS) for flood susceptibility mapping and assessment","volume":"37","author":"Ahmadlou","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"40","DOI":"10.29207\/joseit.v1i2.1995","article-title":"Optimization of Flood Prediction using SVM Algorithm to determine Flood Prone Areas","volume":"1","author":"Dwiasnati","year":"2022","journal-title":"J. Syst. Eng. Inf. Technol. JOSEIT"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Sanders, W., Li, D., Li, W., and Fang, Z.N. (2022). Data-driven flood alert system (FAS) using extreme gradient boosting (XGBoost) to forecast flood stages. Water, 14.","DOI":"10.3390\/w14050747"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2021.05.019","article-title":"Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning","volume":"178","author":"Jiang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_80","first-page":"1025","article-title":"Performance comparison of two deep learning models for flood susceptibility map in Beira area, Mozambique","volume":"25","author":"Ramayanti","year":"2022","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_81","first-page":"102400","article-title":"Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks","volume":"102","author":"Dong","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Tanim, A.H., McRae, C.B., Tavakol-Davani, H., and Goharian, E. (2022). Flood detection in urban areas using satellite imagery and machine learning. Water, 14.","DOI":"10.3390\/w14071140"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"111664","DOI":"10.1016\/j.rse.2020.111664","article-title":"Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine","volume":"240","author":"DeVries","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"8361","DOI":"10.1080\/10106049.2021.2001580","article-title":"Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning","volume":"37","author":"Costache","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1007\/s00704-022-04068-7","article-title":"Urban flood vulnerability assessment in a densely urbanized city using multi-factor analysis and machine learning algorithms","volume":"149","author":"Parvin","year":"2022","journal-title":"Theor. Appl. Climatol."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s11069-022-05336-5","article-title":"Flood vulnerability and buildings\u2019 flood exposure assessment in a densely urbanised city: Comparative analysis of three scenarios using a neural network approach","volume":"113","author":"Pham","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1007\/s12665-021-10013-0","article-title":"An integrated approach for evaluating the flash flood risk and potential erosion using the hydrologic indices and morpho-tectonic parameters","volume":"80","author":"Orabi","year":"2021","journal-title":"Environ. Earth Sci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1007\/s00477-022-02342-8","article-title":"Flood potential mapping by integrating the bivariate statistics, multi-criteria decision-making, and machine learning techniques","volume":"37","author":"Hadian","year":"2023","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Al-Hinai, H., and Abdalla, R. (2021). Mapping coastal flood susceptible areas using shannon\u2019s entropy model: The case of muscat governorate, Oman. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10040252"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"20539517221111361","DOI":"10.1177\/20539517221111361","article-title":"Toward a sociology of machine learning explainability: Human\u2013machine interaction in deep neural network-based automated trading","volume":"9","author":"Borch","year":"2022","journal-title":"Big Data Soc."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"105758","DOI":"10.1016\/j.envsoft.2023.105758","article-title":"Machine learning approach for modeling daily pluvial flood dynamics in agricultural landscapes","volume":"167","author":"Fidan","year":"2023","journal-title":"Environ. Model. Softw."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"102895","DOI":"10.1016\/j.earscirev.2019.102895","article-title":"Outburst floods in China: A review","volume":"197","author":"Liu","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Janizadeh, S., Avand, M., Jaafari, A., Phong, T.V., Bayat, M., Ahmadisharaf, E., Prakash, I., Pham, B.T., and Lee, S. (2019). Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran. Sustainability, 11.","DOI":"10.3390\/su11195426"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/858\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:07:13Z","timestamp":1760105233000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,29]]},"references-count":93,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050858"],"URL":"https:\/\/doi.org\/10.3390\/rs16050858","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,29]]}}}