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Debnath J, Debbarma J, Debnath A, Meraj G, Chand K, Singh SK, Kanga S, Kumar P, Sahariah D, Saikia A. Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm. Environ Monit Assess 2024; 196:110. [PMID: 38172457 DOI: 10.1007/s10661-023-12240-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024]
Abstract
Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.
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Affiliation(s)
- Jatan Debnath
- Department of Geography, Gauhati University, Guwahati, Assam, 781014, India.
| | - Jimmi Debbarma
- Department of Geography & Disaster Management, Tripura University, Agartala, Tripura, India
| | - Amal Debnath
- Department of Forestry & Biodiversity, Tripura University, Agartala, Tripura, India
| | - Gowhar Meraj
- Department of Ecosystem Studies, University of Tokyo, Bunkyo City, Tokyo, Japan
| | - Kesar Chand
- Centre for Environmental Assessment & Climate Change, GB Pant National Institute of Himalayan Environment (NIHE), Himachal Regional Centre (Himachal Pradesh), Kullu, India
| | - Suraj Kumar Singh
- Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur, India
| | - Shruti Kanga
- Department of Geography , Central University of Punjab, Bathinda, India
| | - Pankaj Kumar
- Institute for Global Environmental Strategies, Hayama, Japan
| | | | - Anup Saikia
- Department of Geography, Gauhati University, Guwahati, Assam, 781014, India
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