Hybrid SVM-PSO model to Predict flood discharge in the Jhelum River basin: A case study

Abstract

In view of the susceptibility of the Kashmir valley to flooding, there is an increasing concern about the Jhelum River, a vital tributary of the Indus. In many applications that require a comprehensive flood risk picture at both national and international levels, the ability to carry out extensive flooding risk assessments on a large scale is essential. The vulnerability of many areas to flooding could be exacerbated by more frequent and intensified rainfall events, coupled with the ongoing process of urbanization. Flooding is a frequent occurrence of natural disasters that have an important worldwide impact. The repetition of this disaster has led to substantial economic losses and devastating human suffering, far exceeding the damage caused by most other natural disasters on the global economy. The frequency of floods is increasing significantly and a number of factors, such as climate change, Glacial Melt and Snowmelt, tsunamis, poor river management, soil accumulation, and land use variations such examples may contribute to an increase in the incidence of floods, posing challenges for flood risk management and disaster resilience. India has experienced a series of catastrophic floods in various regions over recent years. Flooding has led to severe human suffering and economic losses, yet there is an urgent need for more advanced flood forecasts and management strategies in order to minimize the growing impact of these natural disasters. In order to address the various aspects of flood risks, adaptation, and mitigation it is therefore necessary to intensify focus on interdisciplinarity in scientific research and innovative solutions. In the Jhelum River basin, this study has introduced an innovative approach for predicting and mitigating floods based on Neural Networks, SVM, and SVM-PSO to build predictive models for flooding forecasts. We are using a wide range of relevant environmental and meteorological data that allows us to assess flood risk in an accurate and efficient way. Statistical techniques such as correlation coefficients analysis, Standard Deviation Assessment, and linear regression are further used to enhance the reliability and comprehensiveness of our analyses which will contribute to the establishment of reliable flood prediction models, as they assist in defining key variables and their dependencies. SVM-PSO showed a remarkable ability in our study, achieving an R2 value of 0.9483, demonstrating that they were able to capture intricate non-linear associations within the dataset. In addition, robust results with an R2 value of 0.9017 were obtained from an SVM which has proved its capacity for optimizing the distribution between data points when dealing with limited and noisy data. Statistical analyses allow the assessment of data variability and linear trends, essential components of accurate flood prediction models, in conjunction with these machine learning methods. This holistic approach, which ultimately promotes disaster resilientness and sustainable development across the region, contributes to developing a robust flood risk prediction model that is significantly improved by effective early warning systems and strategies for managing floods in the Jhelum River basin.