Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: A study of Sundarban Biosphere Reserve, India

Research output: Contribution to journalArticle

Abstract

The Sundarban Biosphere Reserve (SBR), which is one of the important coastal regions of India, is vulnerable to storm surge hazards. It experiences storm surge flood of varying magnitudes every year causing immense loss to life and property. Thus, an accurate storm surge flood susceptibility assessment in the Reserve is essential for safeguarding the coastal communities. The present study attempts to explore the effectiveness of the conventional frequency ratio, modified frequency ratio and support vector machine (SVM) models in storm surge flood susceptibility analysis. Core areas of the SBR and some areas along rivers experience very high and high susceptibility. Moderate susceptibility was prevalent in the north, north-western and some areas in the south-western parts of the Reserve while low susceptibility was observed in the western part of the Reserve (located away from the coast). The maps were validated using the receiver operator characteristic (ROC), the seed cell area index (SCAI), and the spatially agreed area approach. The area under the success rate (0.8347) and prediction rate (0.8221) curves was highest for the SVM model. Frequency ratio and modified frequency ratio models showed 71.2% spatially agreed area of susceptibility. The spatially agreed area of susceptibility was lower (65.1%) in case of modified frequency ratio and the SVM model while it was highest (83.2%) between the modified frequency ratio and SVM models. Thus, the SVM model was found to be the best fit model for analyzing storm surge flood susceptibility in SBR.

Bibliographical metadata

Original languageEnglish
Article number189
JournalCatena
Early online date13 Feb 2020
Publication statusE-pub ahead of print - 13 Feb 2020