Title: Ascertaining the impact of balancing the flood dataset on the performance of classification-based flood forecasting models for the river basins of Odisha

Authors: Vikas Mittal; T.V. Vijay Kumar; Aayush Goel

Addresses: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India ' School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India ' Department of Electronics and Communication Engineering, Bharati Vidyapeeth's College of Engineering, New Delhi, India

Abstract: The climate shift being observed due to global warming has led to an increase in the frequency of natural hazards. Floods, which are the most recurrent and devastating of natural hazards, continue to take their toll on human lives and livelihoods. These losses could be avoided by designing models that can forecast floods at early stages, i.e., before they turn into disasters. This paper focuses on the designing of classification based flood forecasting models for the flood affected districts in the river basins of Odisha. Existing classification based models forecast floods using an imbalanced dataset. This paper attempts to ascertain whether balancing the flood dataset would result in the improvement of the existing classification based flood forecasting models. Experimental results showed that balancing the flood dataset using SMOTE and its variants have resulted in an improvement in the performance of classification based flood forecasting models.

Keywords: natural hazard; floods; disaster; flood forecasting; machine learning; classification; oversampling; SMOTE.

DOI: 10.1504/IJGW.2023.131402

International Journal of Global Warming, 2023 Vol.30 No.3, pp.233 - 254

Received: 19 Sep 2022
Accepted: 07 Nov 2022
Published online: 09 Jun 2023 *

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