{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T21:05:49Z","timestamp":1776459949940,"version":"3.51.2"},"reference-count":56,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University Research Supporting Project","award":["PNURSP2022R24"],"award-info":[{"award-number":["PNURSP2022R24"]}]},{"name":"Deanship of Scientific Research, Qassim University","award":["PNURSP2022R24"],"award-info":[{"award-number":["PNURSP2022R24"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Twenty-two flood-causative factors were nominated based on morphometric, hydrological, soil permeability, terrain distribution, and anthropogenic inferences and further analyzed through the novel hybrid machine learning approach of random forest, support vector machine, gradient boosting, na\u00efve Bayes, and decision tree machine learning (ML) models. A total of 400 flood and nonflood locations acted as target variables of the flood hazard zoning map. All operative factors in this study were tested using variance inflation factor (VIF) values (&lt;5.0) and Boruta feature ranking (&lt;10 ranks) for FHZ maps. The hybrid model along with RF and GBM had sound flood hazard zoning maps for the study area. The area under the receiver operating characteristics (AUROC) curve and statistical model matrices such as accuracy, precision, recall, F1 score, and gain and lift curve were applied to assess model performance. The 70%:30% sample ratio for training and validation of the standalone models concerning the AUROC value showed sound results for all the ML models, such as RF (97%), SVM (91%), GBM (97%), NB (96%), DT (88%), and hybrid (97%). The gain and lift curve also showed the suitability of the hybrid model along with the RF, GBM, and NB models for developing FHZ maps.<\/jats:p>","DOI":"10.3390\/rs14246229","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T03:23:49Z","timestamp":1670556229000},"page":"6229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["Spatial Analysis of Flood Hazard Zoning Map Using Novel Hybrid Machine Learning Technique in Assam, India"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1204-1750","authenticated-orcid":false,"given":"Chiranjit","family":"Singha","sequence":"first","affiliation":[{"name":"Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati University, Sriniketan, Birbhum 731236, West Bengal, India"}]},{"given":"Kishore Chandra","family":"Swain","sequence":"additional","affiliation":[{"name":"Department of Agricultural Engineering, Institute of Agriculture, Visva-Bharati University, Sriniketan, Birbhum 731236, West Bengal, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6271-2730","authenticated-orcid":false,"given":"Modeste","family":"Meliho","sequence":"additional","affiliation":[{"name":"Campus de Nancy, AgroParisTech, 14 Rue Girardet, 54000 Nancy, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9283-3947","authenticated-orcid":false,"given":"Hazem Ghassan","family":"Abdo","sequence":"additional","affiliation":[{"name":"Geography Department, Faculty of Arts and Humanities, Tartous University, Tartous P.O. Box 2147, Syria"},{"name":"Geography Department, Faculty of Arts and Humanities, Damascus University, Damascus P.O. Box 30621, Syria"},{"name":"Geography Department, Faculty of Arts and Humanities, Tishreen University, Lattakia P.O. Box 2237, Syria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8887-915X","authenticated-orcid":false,"given":"Hussein","family":"Almohamad","sequence":"additional","affiliation":[{"name":"Department of Geography, College of Arabic Language and Social Studies, Qassim University, Buraydah 51452, Saudi Arabia"}]},{"given":"Motirh","family":"Al-Mutiry","sequence":"additional","affiliation":[{"name":"Department of Geography, College of Arts, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"141565","DOI":"10.1016\/j.scitotenv.2020.141565","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."},{"key":"ref_2","unstructured":"WHO (World Health Organization) (2022, January 13). Floods. Available online: https:\/\/www.who.int\/health-topics\/floods."},{"key":"ref_3","unstructured":"UNISDR (United Nations Office for Disaster Risk Reduction) (2022, January 21). Economic 1998-2017 Losses, Poverty & DISASTERS, 2017.1-30. Available online: www.unisdr.org."},{"key":"ref_4","unstructured":"NDMA (2022, January 21). (National Disaster Management Authority), Government of India, Floods, Available online: https:\/\/ndma.gov.in\/Natural-Hazards\/Floods."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1007\/s11269-020-02546-z","article-title":"Evaluation of Global Water Resources Reanalysis Data for Estimating Flood Events in the Brahmaputra River Basin","volume":"34","author":"Sultana","year":"2020","journal-title":"Water Resour. Manag."},{"key":"ref_6","unstructured":"NRSC (National Remote Sensing Centre) (2022, January 10). India, Flood Inundation Maps -2022, Available online: https:\/\/www.nrsc.gov.in\/Floods_Inundation_2022?language_content_entity=en."},{"key":"ref_7","unstructured":"RBA (2021, March 14). (Rashtriya Barh Ayog). Flood and Erosion Problem, Available online: https:\/\/waterresources.assam.gov.in\/portlets\/flood-erosion-problems."},{"key":"ref_8","unstructured":"UNISDR (2022, April 13). (United Nations Office for Disaster Risk Reduction). Sendai Framework for Disaster Risk Reduction 2015\u20142030, 2015,1-35, UNISDR\/GE\/2015\u2014ICLUX EN5000 1st edition. Available online: https:\/\/www.unisdr.org."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11069-020-04296-y","article-title":"Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt","volume":"105","author":"Youssef","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1007\/s11069-022-05248-4","article-title":"Evaluation of the prediction capability of AHP and F-AHP methods in flood susceptibility mapping of Ernakulam district (India)","volume":"112","author":"Vilasan","year":"2022","journal-title":"Nat. Hazards"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gupta, L., and Dixit, J. (2022). A GIS-based flood risk mapping of Assam, India, using the MCDA-AHP approach at the regional and administrative level. Geocarto Int.","DOI":"10.21203\/rs.3.rs-1015728\/v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Swain, K.C., Singha, C., and Nayak, L. (2020). Flood Susceptibility Mapping through the GIS-AHP Technique Using the Cloud. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9120720"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Parsian, S., Amani, M., Moghimi, A., Ghorbanian, A., and Mahdavi, S. (2021). Flood Hazard Mapping Using Fuzzy Logic, Analytical Hierarchy Process, and Multi-Source Geospatial Datasets. Remote Sens., 13.","DOI":"10.3390\/rs13234761"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Szul, T., Tabor, S., and Pancerz, K. (2021). Application of the BORUTA Algorithm to Input Data Selection for a Model Based on Rough Set Theory (RST) to Prediction Energy Consumption for Building Heating. Energies, 14.","DOI":"10.3390\/en14102779"},{"key":"ref_15","first-page":"134979","article-title":"Modeling flood susceptibility using data-driven approaches of na\u00efve Bayes tree, alternating decision tree, and random forest methods","volume":"701","author":"Hen","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_16","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":"2020","journal-title":"Geosci. Front."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"659296","DOI":"10.3389\/feart.2021.659296","article-title":"Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach","volume":"9","author":"Pandey","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1080\/19475705.2021.1920480","article-title":"Detection of areas prone to flood risk using state-of-the-art machine learning models","volume":"12","author":"Costache","year":"2021","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.2166\/wcc.2022.435","article-title":"Assessment of flood susceptibility prediction based on optimized tree-based machine learning models","volume":"13","author":"Eslaminezhad","year":"2022","journal-title":"J. Water Clim. Chang."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110485","DOI":"10.1016\/j.jenvman.2020.110485","article-title":"Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment","volume":"265","author":"Costache","year":"2020","journal-title":"J. Environ. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.2166\/wcc.2019.321","article-title":"Flood prediction based on weather parameters using deep learning","volume":"11","author":"Sankaranarayanan","year":"2020","journal-title":"J. Water Clim. Chang."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101687","DOI":"10.1016\/j.ijdrr.2020.101687","article-title":"Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts","volume":"50","author":"Eini","year":"2020","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4621","DOI":"10.1007\/s11269-021-02972-7","article-title":"Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling","volume":"35","author":"Janizadeh","year":"2021","journal-title":"Water Resour. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ahmadlou, M., Ghajari, Y.E., and Karimi, M. (2022). Enhanced Classification and Regression Tree (Cart) by Genetic Algorithm (Ga) and Grid Search (Gs) for Flood Susceptibility Mapping and Assessment. Geocarto Int.","DOI":"10.1080\/10106049.2022.2082550"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Janizadeh, S., Avand, M., Jaafari, A., Van Phong, T., 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"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101498","DOI":"10.1016\/j.ecoinf.2021.101498","article-title":"Flood susceptibility mapping using extremely randomized trees for Assam 2020 floods","volume":"67","author":"Sachdeva","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4571","DOI":"10.1080\/10106049.2021.1892209","article-title":"Novel ensemble machine learning models in flood susceptibility mapping","volume":"37","author":"Prasad","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"e12683","DOI":"10.1111\/jfr3.12683","article-title":"Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks","volume":"14","author":"Ahmadlou","year":"2020","journal-title":"J. Flood Risk Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1007\/s11069-021-04877-5","article-title":"Flash flood susceptibility prediction mapping for a road network using hybrid machine learning models","volume":"109","author":"Ha","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"135161","DOI":"10.1016\/j.scitotenv.2019.135161","article-title":"Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method","volume":"711","author":"Hosseini","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1080\/19475705.2019.1615005","article-title":"A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China","volume":"10","author":"Xi","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_34","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","year":"2020","journal-title":"Nat. Hazar."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/19475705.2018.1506509","article-title":"Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods","volume":"10","author":"Tehrany","year":"2018","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104536","DOI":"10.1016\/j.catena.2020.104536","article-title":"Flood susceptibility assessment based on a novel random Na\u00efve Bayes method: A comparison between different factor discretization methods","volume":"190","author":"Tang","year":"2020","journal-title":"Catena"},{"key":"ref_37","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_38","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_39","first-page":"101951","article-title":"Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features","volume":"84","author":"Goffi","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/s13201-021-01450-0","article-title":"Assessment of soil erosion, flood risk and groundwater potential of Dhanari watershed using remote sensing and geographic information system, district Uttarkashi, Uttarakhand, India","volume":"11","author":"Rawat","year":"2021","journal-title":"Appl. Water Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101211","DOI":"10.1016\/j.ijdrr.2019.101211","article-title":"Flood susceptibility modeling and hazard perception in Rwanda","volume":"38","author":"Li","year":"2019","journal-title":"Int. J. Disas Risk Reduc."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Theobald, D.M., Harrison-Atlas, D., Monahan, W.B., and Albano, C.M. (2015). Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0143619"},{"key":"ref_43","unstructured":"Kennedy, C.M., Oakleaf, J.R., Theobald, D.M., Baruch-Mordo, S., and Kiesecker, J. (2021, January 13). Global Human Modification of Terrestrial Systems. 2020, Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Available online: https:\/\/sedac.ciesin.columbia.edu\/data\/set\/lulc-human-modification-terrestrial-systems."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Saha, S., Roy, J., Arabameri, A., Blaschke, T., and Tien Bui, D. (2020). Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India. Sensors, 20.","DOI":"10.3390\/s20051313"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, J., Li, Q., Wang, H., and Deng, M. (2020). A machine learning ensemble approach based on random forest and radial basis function neural network for risk evaluation of regional flood disaster: A case study of the yangtze river delta, China. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17010049"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s12145-020-00530-0","article-title":"Flood susceptibility assessment using extreme gradient boosting (EGB)","volume":"14","author":"Mirzaei","year":"2020","journal-title":"Iran. Earth Sci. Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s12517-022-09531-3","article-title":"Random forest and na\u00efve Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt","volume":"15","year":"2022","journal-title":"Arab. J. Geosci."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ha-Minh, C., Tang, A.M., Bui, T.Q., Vu, X.H., and Huynh, D.V.K. (2022). Using Decision Tree J48 Based Machine Learning Algorithm for Flood Susceptibility Mapping: A Case Study in Quang Binh Province, Vietnam. CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure. Lecture Notes in Civil Engineering, Springer.","DOI":"10.1007\/978-981-16-7160-9"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, J., Wang, J., Xiong, J., Cheng, W., Sun, H., Yong, Z., and Wang, N. (2021). Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets. Remote Sens., 13.","DOI":"10.3390\/rs13234945"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lombana, L., and Mart\u00ednez-Gra\u00f1a, A. (2022). A Flood Mapping Method for Land Use Management in Small-Size Water Bodies: Validation of Spectral Indexes and a Machine Learning Technique. Agronomy, 12.","DOI":"10.3390\/agronomy12061280"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Song, D., Zhang, Q., Wang, B., Yin, C., and Xia, J. (2022). A Novel Dual Branch Neural Network Model for Flood Monitoring in South Asia Based on CYGNSS Data. Remote Sens., 14.","DOI":"10.3390\/rs14205129"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Askar, S., Zeraat Peyma, S., Yousef, M.M., Prodanova, N.A., Muda, I., Elsahabi, M., and Hatamiafkoueieh, J. (2022). Flood Susceptibility Mapping Using Remote Sensing and Integration of Decision Table Classifier and Metaheuristic Algorithms. Water, 14.","DOI":"10.3390\/w14193062"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105114","DOI":"10.1016\/j.catena.2020.105114","article-title":"Flood spatial prediction modeling using a hybrid of meta optimization and support vector regression modeling","volume":"199","author":"Panahi","year":"2021","journal-title":"Catena"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J.J., Geertsema, M., Khosravi, K., Amini, A., and Bahrami, S. (2020). Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sens., 12.","DOI":"10.3390\/rs12020266"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, Y.J., Lin, H.-J., Liou, J.-J., Cheng, C.-T., and Chen, Y.-M. (2022). Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan. Water, 14.","DOI":"10.3390\/w14020207"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:51Z","timestamp":1760146611000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,8]]},"references-count":56,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246229"],"URL":"https:\/\/doi.org\/10.3390\/rs14246229","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,8]]}}}