Prediction of flow discharge in Mahanadi River Basin, India, based on novel hybrid SVM approaches

Sandeep Samantaray () and Abinash Sahoo ()
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Sandeep Samantaray: NIT Silchar
Abinash Sahoo: NIT Silchar

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 7, No 92, 18699-18723

Abstract: Abstract Accurate monthly flow discharge prediction can yield significant evidence for sustainable management of water resources systems, optimal water allocation and use, mitigating flood events, and warning against famine. Inspiration to explore and develop skillful prediction models is a continuing attempt for various hydrologic assessments. The main aim of present study is to explore the potential of novel hybrid PSR-SVM-FFA model (integration of phase space reconstruction with support vector machine and firefly algorithm) and assess its performance against conventional radial basis function network, SVM, and hybrid SVM-FFA to predict flow discharge considering data from four gauge stations of Mahanadi River basin, India. PSR is applied to extract information and characteristics from flow time series and improve accuracy of hybrid SVM-FFA model. For assessing the model’s enactment, Nash–Sutcliffe coefficient root-mean-square error and Willmott’s Index (WI) indicators are calculated. The results showed that PSR-SVM-FFA model generated improved monthly flow predictions than other applied methods. The result indicates that best values of WI are 0.912–0.929, 0.949–0.956, 0.961–0.967, and 0.98–0.984 for RBFN, SVM, SVM-FFA, and PSR-SVM-FFA, respectively. This demonstrates that PSR-SVM-FFA provides prominent predictions compared to the other three approaches.

Keywords: Flow discharge; RBFN; SVM; SVM-FFA; PSR-SVM-FFA; Mahanadi basin (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-023-03412-9

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