Modeling of impact assessment of super cyclone Amphan with machine learning algorithms in Sundarban Biosphere Reserve, India

Tania Nasrin (), Mohd Ramiz (), Md Nawaj Sarif (), Mohd Hashim (), Masood Ahsan Siddiqui (), Lubna Siddiqui (), Sk Mohibul () and Sakshi Mankotia ()
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Tania Nasrin: Jamia Millia Islamia
Mohd Ramiz: Jamia Millia Islamia
Md Nawaj Sarif: Jamia Millia Islamia
Mohd Hashim: Jamia Millia Islamia
Masood Ahsan Siddiqui: Jamia Millia Islamia
Lubna Siddiqui: Jamia Millia Islamia
Sk Mohibul: Jamia Millia Islamia
Sakshi Mankotia: Jamia Millia Islamia

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 117, issue 2, No 31, 1945-1968

Abstract: Abstract Coastal areas play an important role in the global food and economic system, but are vulnerable to a number of coastal hazards such as cyclones and storm surges. The Sundarban Biosphere Reserve (SBR) is located on the east coast of India and has a rich diversity of aquatic and terrestrial flora and fauna. The region is frequently affected by coastal hazards such as cyclones and storm surges. The objective of this study is to investigate the impact of Super Cyclone Amphan on land use land cover (LULC) in SBR. For this purpose, the land use land cover (LULC) map of the study area before and after the cyclone was first constructed using four machine learning algorithms: Support Vector Machine (SVM), Spectral Angle Mapper and Maximum Likelihood Classifier. In addition, the accuracy of these methods was evaluated using the confusion matrix. The result shows that the SVM basis provides better accuracy than the other methods. After evaluating the accuracy, the detection of changes in the LULC was analysed. The result shows that forest cover in the region decreased significantly (about 38.8%) due to super cyclone Amphan. In addition, agricultural land, swamps, sandbanks and beaches increased by 49.51%, 39.57%, 17.40% and 6.93% respectively. The findings of this study can be used by local and state disaster management authorities to prepare the effective disaster management plans for the region.

Keywords: Land use land cover; Amphan super cyclone; Impact assessment; Machine learning classifiers; Sundarban (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-05935-w

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