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

Water

Somayeh Panahi¹  Asish Saha²  Indrajit Chowdhuri²  Rabin Chakrabortty²  SubodhChandra Pal²  Thomas Blaschke³  Alireza Arabameri⁴  Romulus Costache⁵  Aman Arora⁶ 

[1] Department of Computer Engineering, Faculty of Valiasr, Tehran Branch, Technical and Vocational University (TVU), Tehran 14356-61137, Iran;Department of Geography, The University of Burdwan, Bardhaman 713104, West Bengal, India;Department of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, Austria;Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran;Research Institute of the University of Bucharest, 90–92 Sos. Panduri, 5th District, 050107 Bucharest, Romania;University Center for Research & Development (UCRD), Chandigarh University, Mohali 140413, Punjab, India;

关键词: flood susceptibility assessment;    Koiya River basin;    hyperpipes (HP);    support vector regression (SVR);    ensemble approach;   

DOI  :  10.3390/w13020241

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown