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Open AccessArticle

Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

1
Department of Geography, The University of Burdwan, Bardhaman 713104, West Bengal, India
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Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran
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Department of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, Austria
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Department of Computer Engineering, Faculty of Valiasr, Tehran Branch, Technical and Vocational University (TVU), Tehran 14356-61137, Iran
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Research Institute of the University of Bucharest, 90–92 Sos. Panduri, 5th District, 050107 Bucharest, Romania
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National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686 Bucharest, Romania
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University Center for Research & Development (UCRD), Chandigarh University, Mohali 140413, Punjab, India
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Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi 110025, Delhi, India
*
Authors to whom correspondence should be addressed.
Water 2021, 13(2), 241; https://doi.org/10.3390/w13020241
Received: 8 November 2020 / Revised: 1 January 2021 / Accepted: 3 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Flash-Flood Susceptibility, Forecast and Warning)
Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871). View Full-Text
Keywords: flood susceptibility assessment; Koiya River basin; hyperpipes (HP); support vector regression (SVR); ensemble approach flood susceptibility assessment; Koiya River basin; hyperpipes (HP); support vector regression (SVR); ensemble approach
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MDPI and ACS Style

Saha, A.; Pal, S.C.; Arabameri, A.; Blaschke, T.; Panahi, S.; Chowdhuri, I.; Chakrabortty, R.; Costache, R.; Arora, A. Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms. Water 2021, 13, 241. https://doi.org/10.3390/w13020241

AMA Style

Saha A, Pal SC, Arabameri A, Blaschke T, Panahi S, Chowdhuri I, Chakrabortty R, Costache R, Arora A. Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms. Water. 2021; 13(2):241. https://doi.org/10.3390/w13020241

Chicago/Turabian Style

Saha, Asish; Pal, Subodh C.; Arabameri, Alireza; Blaschke, Thomas; Panahi, Somayeh; Chowdhuri, Indrajit; Chakrabortty, Rabin; Costache, Romulus; Arora, Aman. 2021. "Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms" Water 13, no. 2: 241. https://doi.org/10.3390/w13020241

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