{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:36:36Z","timestamp":1774539396888,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T00:00:00Z","timestamp":1744156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-03836-2","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T11:32:05Z","timestamp":1744371125000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Flood Susceptibility Assessment in the Teesta River Basin, India: An Advanced Geospatial Artificial Intelligence Approach Leveraging Spatial Data Augmentation"],"prefix":"10.1007","volume":"6","author":[{"given":"Deepanjan","family":"Sen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4837-3020","authenticated-orcid":false,"given":"Swarup","family":"Das","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"key":"3836_CR1","unstructured":"Delforge D, et al.: CRED: EM-DAT The International Disaster Database, Centre for Research on the Epidemiology of Disasters (CRED). http:www.emdat.be"},{"key":"3836_CR2","unstructured":"Flood Damage Statistics (Statewise and for the Country as a whole) during 1953 to 2020, Central Water Commission, Ministry of jal shakti, Department of Water Resources, River Development and Ganga Rejuvenation, GoI. http:\/\/cwc.gov.in\/."},{"key":"3836_CR3","unstructured":"Mahalanobis PC. Report on rainfall and flood in North Bengal during the period 1870\u20131922. In: Proceedings of the Indian Science Congress (Bombay). Indian Science Congress (Bombay); 1927. ."},{"issue":"146","key":"3836_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2020.104619","volume":"1","author":"R Datla","year":"2021","unstructured":"Datla R, Mohan CK. A novel framework for seamless mosaic of Cartosat-1 DEM scenes. Comput Geosci. 2021;1(146): 104619. https:\/\/doi.org\/10.1016\/j.cageo.2020.104619.","journal-title":"Comput Geosci"},{"issue":"1","key":"3836_CR5","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/s42979-020-00442-2","volume":"2","author":"BZ Demiray","year":"2021","unstructured":"Demiray BZ, Sit M, Demir I. D-SRGAN: DEM super-resolution with generative adversarial networks. SN Comput Sci. 2021;2(1):48. https:\/\/doi.org\/10.1007\/s42979-020-00442-2.","journal-title":"SN Comput Sci."},{"issue":"16","key":"3836_CR6","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1007\/s12665-024-11751-7","volume":"83","author":"S Rehman","year":"2024","unstructured":"Rehman S, Chaudhary BS, Azhoni A. Appraisal of flood susceptibility of Hooghly basin, India using Shannon entropy (SE) and fuzzy analytical hierarchy process (FAHP). Environ Earth Sci. 2024;83(16):462. https:\/\/doi.org\/10.1007\/s12665-024-11751-7.","journal-title":"Environ Earth Sci"},{"issue":"4","key":"3836_CR7","doi-asserted-by":"publisher","first-page":"669","DOI":"10.3390\/atmos14040669","volume":"14","author":"T Basu","year":"2023","unstructured":"Basu T, Mondal BK, Abdelrahman K, Fnais MS, Praharaj S. Assessing Urban flood hazard vulnerability using multi-criteria decision making and geospatial techniques in nabadwip municipality. West Bengal in India Atmosphere. 2023;14(4):669. https:\/\/doi.org\/10.3390\/atmos14040669.","journal-title":"West Bengal in India. Atmosphere."},{"issue":"7","key":"3836_CR8","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.3390\/w14071049","volume":"14","author":"S Gayen","year":"2022","unstructured":"Gayen S, Villalta IV, Haque SM. Flood risk assessment and its mapping in Purba Medinipur District, West Bengal. India Water. 2022;14(7):1049. https:\/\/doi.org\/10.3390\/w14071049.","journal-title":"India. Water."},{"issue":"6","key":"3836_CR9","doi-asserted-by":"publisher","first-page":"16036","DOI":"10.1007\/s11356-022-23168-5","volume":"30","author":"R Mitra","year":"2022","unstructured":"Mitra R, Das J. A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India. Environ Sci Pollut Res. 2022;30(6):16036\u201367. https:\/\/doi.org\/10.1007\/s11356-022-23168-5.","journal-title":"Environ Sci Pollut Res"},{"issue":"1","key":"3836_CR10","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s40808-018-0427-z","volume":"4","author":"RK Samanta","year":"2018","unstructured":"Samanta RK, Bhunia GS, Shit PK, Pourghasemi HR. Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin. India Model Earth Syst Environ. 2018;4(1):395\u2013408. https:\/\/doi.org\/10.1007\/s40808-018-0427-z.","journal-title":"India. Model Earth Syst Environ"},{"issue":"8","key":"3836_CR11","doi-asserted-by":"publisher","first-page":"297","DOI":"10.3390\/ijgi13080297","volume":"13","author":"A Sharma","year":"2024","unstructured":"Sharma A, Poonia M, Rai A, Biniwale RB, Tu\u00a8gel F, Holzbecher E, et al. Flood susceptibility mapping using GIS-based frequency ratio and Shannon\u2019s entropy index bivariate statistical models: a case study of Chandrapur District India. ISPRS Int J Geo-Inform. 2024;13(8):297. https:\/\/doi.org\/10.3390\/ijgi13080297.","journal-title":"ISPRS Int J Geo-Inform."},{"issue":"22","key":"3836_CR12","doi-asserted-by":"publisher","first-page":"3771","DOI":"10.3390\/w14223771","volume":"14","author":"U Pawar","year":"2022","unstructured":"Pawar U, Suppawimut W, Muttil N, Rathnayake U. A GIS-Based comparative analysis of frequency ratio and statistical index models for flood susceptibility mapping in the Upper Krishna Basin, India. Water. 2022;14(22):3771. https:\/\/doi.org\/10.3390\/w14223771.","journal-title":"Water"},{"issue":"25","key":"3836_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.rsase.2021.100686","volume":"1","author":"S Bera","year":"2022","unstructured":"Bera S, Das A, Mazumder T. Evaluation of machine learning, information theory and multi-criteria decision analysis methods for flood susceptibility mapping under varying spatial scale of analyses. Remote Sens Appl. 2022;1(25): 100686. https:\/\/doi.org\/10.1016\/j.rsase.2021.100686.","journal-title":"Remote Sens Appl."},{"key":"3836_CR14","unstructured":"Achu AL, Gopinath G, Surendran U.: Machine Learning Framework for Flood Susceptibility Modeling in a Fast-Growing Urban City of Southern India."},{"key":"3836_CR15","unstructured":"Pathan AI, Agnihotri DPG, Said DS, Patel DD, Prieto DC, Mohsini U, et al.: Flood risk mapping using multi-criteria analysis (TOPSIS) model through geospatial techniques. A case study of the Navsari city, Gujarat, India."},{"key":"3836_CR16","unstructured":"Mukhopadhyay SC. The Tista Basin: A Study in Fluvial Geomorphology. K.P. Bagchi; 1984."},{"key":"3836_CR17","unstructured":"Majumdar RC. History of Ancient Bengal. G. Bharadwaj , Calcutta; 1971."},{"key":"3836_CR18","unstructured":"Rudra K.: Rivers of the Ganga\u2013Brahmaputra\u2013Meghna Delta: An Overview. Springer, Cham."},{"issue":"6","key":"3836_CR19","doi-asserted-by":"publisher","first-page":"3251","DOI":"10.3390\/su14063251","volume":"14","author":"G Antzoulatos","year":"2022","unstructured":"Antzoulatos G, Kouloglou IO, Bakratsas M, Moumtzidou A, Gialampoukidis I, Karakostas A, et al. Flood hazard and risk mapping by applying an explainable machine learning framework using satellite imagery and GIS data. Sustainability. 2022;14(6):3251. https:\/\/doi.org\/10.3390\/su14063251.","journal-title":"Sustainability"},{"key":"3836_CR20","doi-asserted-by":"publisher","unstructured":"Arabameri A, Rezaei K, Cerd`a A, Conoscenti C, Kalantari Z. A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Science of The Total Environment. 2019;660:443\u2013458. https:\/\/doi.org\/10.1016\/j.scitotenv.2019.01.021","DOI":"10.1016\/j.scitotenv.2019.01.021"},{"issue":"14","key":"3836_CR21","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.rsase.2019.02.006","volume":"4","author":"S Das","year":"2019","unstructured":"Das S. Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens Appl. 2019;4(14):60\u201374. https:\/\/doi.org\/10.1016\/j.rsase.2019.02.006.","journal-title":"Remote Sens Appl."},{"issue":"1","key":"3836_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/hyp.3360050103","volume":"5","author":"ID Moore","year":"1991","unstructured":"Moore ID, Grayson RB, Ladson AR. Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol Processes. 1991;5(1):3\u201330. https:\/\/doi.org\/10.1002\/hyp.3360050103.","journal-title":"Hydrol Processes"},{"issue":"3","key":"3836_CR23","doi-asserted-by":"publisher","first-page":"101","DOI":"10.3390\/ijgi8030101","volume":"8","author":"F Dohnal","year":"2019","unstructured":"Dohnal F, Hubacek M, Simkova K. Detection of microrelief objects to impede the movement of vehicles in Terrain. ISPRS Int J Geo-Inform. 2019;8(3):101. https:\/\/doi.org\/10.3390\/ijgi8030101.","journal-title":"ISPRS Int J Geo-Inform"},{"issue":"1","key":"3836_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1086\/627044","volume":"73","author":"MA Melton","year":"1965","unstructured":"Melton MA. The geomorphic and paleoclimatic significance of alluvial deposits in Southern Arizona. J Geol. 1965;73(1):1\u201338. https:\/\/doi.org\/10.1086\/627044.","journal-title":"J Geol"},{"key":"3836_CR25","doi-asserted-by":"publisher","unstructured":"Gallant JC, Dowling TI. A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research. 2003 12;39(12). https:\/\/doi.org\/10.1029\/2002WR001426","DOI":"10.1029\/2002WR001426"},{"key":"3836_CR26","doi-asserted-by":"publisher","unstructured":"Grover R, Sharma S, Jindal P, Kumar N, Verma A. Trend Analysis of Rainfall for Multi-Purpose Water Resources Projects Using Machine Learning Predictive Model-ARIMA. SN Computer Science. 2024 11;5(8):1110. https:\/\/doi.org\/10.1007\/s42979-024-03511-y.","DOI":"10.1007\/s42979-024-03511-y"},{"key":"3836_CR27","unstructured":"B\u00a8ohner J, Selige T. Spatial prediction of soil attributes using terrain analysis and climate regionalisation. In: and others, editor. SAGA-Analyses and modelling applications; 2006."},{"key":"3836_CR28","unstructured":"Hu G, Dai W, Xiong L, Tang G. Classification of Terrain Concave and Convex Landform Units by using TIN. In: Proceedings of the Geomorphometry 2020 Conference; 2020."},{"issue":"182","key":"3836_CR29","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.geomorph.2012.11.005","volume":"1","author":"J Jasiewicz","year":"2013","unstructured":"Jasiewicz J, Stepinski TF. Geomorphons\u2014a pattern recognition approach to classification and mapping of landforms. Geomorphology. 2013;1(182):147\u201356. https:\/\/doi.org\/10.1016\/j.geomorph.2012.11.005.","journal-title":"Geomorphology"},{"key":"3836_CR30","doi-asserted-by":"crossref","unstructured":"Kursa BM, Rudnicki RW. Feature Selection with the Boruta Package. J Stat Softw. 2010;36(11).","DOI":"10.18637\/jss.v036.i11"},{"key":"3836_CR31","doi-asserted-by":"crossref","unstructured":"Guo G, Wang H, Bell D, Bi Y, Greer K. KNN Model-Based Approach in Classification. In: On the move to meaningful internet systems 2003: CoopIS, DOA, and ODBASE. Springer; 2003. 986\u2013996.","DOI":"10.1007\/978-3-540-39964-3_62"},{"issue":"1","key":"3836_CR32","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans Inform Theory. 1967;13(1):21\u20137. https:\/\/doi.org\/10.1109\/TIT.1967.1053964.","journal-title":"IEEE Trans Inform Theory"},{"issue":"3","key":"3836_CR33","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1007\/s00500-020-05297-6","volume":"25","author":"I Wickramasinghe","year":"2021","unstructured":"Wickramasinghe I, Kalutarage H. Naive Bayes: applications, variations and vulnerabilities: a review of literature with code snippets for implementation. Soft Comput. 2021;25(3):2277\u201393. https:\/\/doi.org\/10.1007\/s00500-020-05297-6.","journal-title":"Soft Comput"},{"key":"3836_CR34","doi-asserted-by":"crossref","unstructured":"Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification And Regression Trees. Routledge; 2017.","DOI":"10.1201\/9781315139470"},{"issue":"3","key":"3836_CR35","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1198\/106186006X133933","volume":"15","author":"T Hothorn","year":"2006","unstructured":"Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 2006;15(3):651\u201374. https:\/\/doi.org\/10.1198\/106186006X133933.","journal-title":"J Comput Graph Stat"},{"key":"3836_CR36","doi-asserted-by":"crossref","unstructured":"Bu\u00a8hlmann P, Hothorn T (2007) Boosting algorithms: regularization, prediction and model fitting. Stat Sci 22(4).","DOI":"10.1214\/07-STS242"},{"key":"3836_CR37","doi-asserted-by":"crossref","unstructured":"Ripley BD. Pattern Recognition and Neural Networks. Cambridge University Press; 1996.","DOI":"10.1017\/CBO9780511812651"},{"issue":"1","key":"3836_CR38","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","volume":"17","author":"A Tharwat","year":"2021","unstructured":"Tharwat A. Classification assessment methods. Appl Comput Inform. 2021;17(1):168\u201392. https:\/\/doi.org\/10.1016\/j.aci.2018.08.003.","journal-title":"Appl Comput Inform."},{"key":"3836_CR39","doi-asserted-by":"publisher","first-page":"110020","DOI":"10.1016\/j.asoc.2023.110020","volume":"134","author":"AE Yilmaz","year":"2023","unstructured":"Yilmaz AE, Demirhan H. Weighted kappa measures for ordinal multi-class classification performance. Appl Soft Comput. 2023;134:110020. https:\/\/doi.org\/10.1016\/j.asoc.2023.110020.","journal-title":"Appl Soft Comput"},{"key":"3836_CR40","doi-asserted-by":"crossref","unstructured":"Canbek G, Sagiroglu S, Temizel TT, Baykal N. Binary classification performance measures\/metrics: A comprehensive visualized roadmap to gain new insights. In: 2017 International conference on computer science and engineering (UBMK). IEEE; 2017. p. 821\u2013826.","DOI":"10.1109\/UBMK.2017.8093539"},{"key":"3836_CR41","unstructured":"Ling CX, Huang J, Zhang H.: AUC: A Better Measure than Accuracy in Comparing Learning Algorithms."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03836-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-03836-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-03836-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T05:16:28Z","timestamp":1745298988000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-03836-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,9]]},"references-count":41,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["3836"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-03836-2","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,9]]},"assertion":[{"value":"15 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors assert the absence of competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"The research study did not contain any participation of human being or animals.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"368"}}