Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models

Arvind Yadav, Premkumar Chithaluru, Aman Singh (), Marwan Ali Albahar, Anca Jurcut, Roberto Marcelo Álvarez, Ramesh Kumar Mojjada and Devendra Joshi
Additional contact information
Arvind Yadav: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Andhra Pradesh, India
Premkumar Chithaluru: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Andhra Pradesh, India
Aman Singh: Higher Polytechnic School, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
Marwan Ali Albahar: Department of Computer Science, Umm Al Qura University, Mecca P.O. Box 715, Saudi Arabia
Anca Jurcut: School of Computer Science, University College Dublin, Belfield, D04 V1W8 Dublin, Ireland
Roberto Marcelo Álvarez: Department of Project Management, Universidad Internacional Iberoamericana, Campeche C.P. 24560, Mexico
Ramesh Kumar Mojjada: Renault Nissan Technology & Business Centre, Chengalpattu 603002, Tamil Nadu, India
Devendra Joshi: Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522502, Andhra Pradesh, India

Mathematics, 2022, vol. 10, issue 22, 1-22

Abstract: Rivers play a major role within ecosystems and society, including for domestic, industrial, and agricultural uses, and in power generation. Forecasting of suspended sediment yield (SSY) is critical for design, management, planning, and disaster prevention in river basin systems. It is difficult to forecast the SSY using conventional methods because these approaches cannot handle complicated non-stationarity and non-linearity. Artificial intelligence techniques have gained popularity in water resources due to handling complex problems of SSY. In this study, a fully automated generalized single hybrid intelligent artificial neural network (ANN)-based genetic algorithm (GA) forecasting model was developed using water discharge, temperature, rainfall, SSY, rock type, relief, and catchment area data of eleven gauging stations for forecasting the SSY. It is applied at individual gauging stations for SSY forecasting in the Mahanadi River which is one of India’s largest peninsular rivers. All parameters of the ANN are optimized automatically and simultaneously using the GA. The multi-objective algorithm was applied to optimize the two conflicting objective functions (error variance and bias). The mean square error objective function was considered for the single-objective optimization model. Single and multi-objective GA-based ANN, autoregressive and multivariate autoregressive models were compared to each other. It was found that the single-objective GA-based ANN model provided the best accuracy among all comparative models, and it is the most suitable substitute for forecasting SSY. If the measurement of SSY is unavailable, then single-objective GA-based ANN modeling approaches can be recommended for forecasting SSY due to comparatively superior performance and simplicity of implementation.

Keywords: Mahanadi River; water discharge; genetic algorithm; ANN; SSY (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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