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

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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.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors are thankful to the United States Geological Survey (USGS) for giving the free access to the Satellite data. Authors are thankful to the anonymous reviewers for making the scholarly comments which helped in improving the manuscript many-fold.

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  1. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India

    Tania Nasrin, Mohd Ramiz, Md Nawaj Sarif, Mohd Hashim, Masood Ahsan Siddiqui, Lubna Siddiqui, Sk Mohibul & Sakshi Mankotia

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  1. Tania Nasrin
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Contributions

TN and MNS participated in research design, data collection, data analysis and manuscript writing. MR and MH participated in the data analysis and validation. MAS and LS helped in reviewed the manuscript. SM and SM helped in literature survey and validation.

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Correspondence to Md Nawaj Sarif.

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Nasrin, T., Ramiz, M., Sarif, M.N. et al. Modeling of impact assessment of super cyclone Amphan with machine learning algorithms in Sundarban Biosphere Reserve, India. Nat Hazards 117, 1945–1968 (2023). https://doi.org/10.1007/s11069-023-05935-w

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