Hybrid SVM-PSO Approach for Flood Prediction in the Jhelum River Basin: A Case Study of Kupwara District

The Jhelum River, which is the major river of the signal, contributes to the Indus River system as one of the notable tributaries and is bestowed with crucial importance in adhering its water for various uses, including irrigation, hydropower supply and domestic purposes. However, it is very vulnerable to serious floods that cause many losses of life and property. As a result, precise flood forecasting in the Jhelum River is essential to facilitate proper disaster response and prevention strategies. Flood forecasting is critical to disaster preparedness, particularly in countries vulnerable to repeated hydrologic disasters. This research aims to improve the flood forecasting technique applicable to the Kupwara district of Jammu and Kashmir, as the area is frequently ravaged by floods, mostly occasioned by its geographical and climatic attributes. We employ hydrometeorological data from January 1975 to December 2023 to investigate the interaction of the factors that determine flood occurrence, and we evaluate the capability of data-based models in providing reliable monthly flood predictions. This study proposes Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and finally, the hybrid model SVM-PSO (Particle Swarm Optimization) to predict flood events in the Jhelum River. The results of the hybrid SVM-PSO model show maximum goodness of fit with an R² value of 0.9562, minimum MSE value of 9.2237 and Nash-Sutcliffe Model Efficiency of 0.9483. These outcomes illustrate the model's strengths for flood forecasting for Kupwara; its application to disaster risk reduction is valuable. The study’s findings highlight the possibility of extending the applications of progressive AI tools to reduce the effects of flooding and preserve the areas’ susceptible populations and assets in the Kupwara district.

This research was supported by the Empowerment and Equity Opportunities for Excellence (EEQ) in Science (Dr SS) under SERB, Govt. of India, under grant no. EEQ/2023/000585