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Article

Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm

1
Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram 522302, India
2
Faculty of Information Sciences and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
3
Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia
4
Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
5
Department of Civil Engineering, Gandhi Institute for Technology, Bhubaneswar 752101, India
*
Author to whom correspondence should be addressed.
Academic Editor: Bommanna Krishnappan
Water 2022, 14(18), 2815; https://doi.org/10.3390/w14182815
Received: 4 August 2022 / Revised: 2 September 2022 / Accepted: 6 September 2022 / Published: 9 September 2022
(This article belongs to the Special Issue AI and Deep Learning Applications for Water Management)
Rivers are the agents on earth and act as the main pathways for transporting the continental weathered materials into the sea. The estimation of suspended sediment yield (SSY) is important in the design, planning and management of water resources. The SSY depends on many factors and their interrelationships, which are very nonlinear and complex. The traditional approaches are unable to solve these complex nonlear processes of SSY. Thus, the development of a reliable and accurate model for estimating the SSY is essential. The goal of this research was to develop a single hybrid artificial intelligence model, which is a hybridization of the artificial neural network (ANN) and genetic algorithm (GA) (ANN-GA) for the estimation of SSY in the Mahanadi River (MR), India, by combining data from 11-gauge stations into a single hybrid generalized model and applying it to every gauging station for estimating the SSY. All parameters of the ANN model were optimized automatically and simultaneously using GA to estimate the SSY. The proposed model was developed considering the temporal monthly hydro-climatic data, such as temperature (T), rainfall (RF), water discharge (Q) and SSY and spatial data, including the rock type (RT), catchment area (CA) and relief (R), of all 11 gauging stations in the MR. The performances of the conventional sediment rating curve (SRC), ANN and multiple linear regression (MLR) were compared with the hybrid ANN-GA model. It was noticed that the ANN-GA model provided with greatest coefficient of correlation (0.8710) and lowest root mean square error (0.0088) values among all comparative SRC, ANN and MLR. Thus, the proposed ANN-GA is most appropriate model compared to other examined models for estimating SSY in the MR Basin, India, particularly at the Tikarapara measuring station. If no measures of SSY are available in the MR, then the modelling approach could be used to estimate SSY at ungauged or gauge stations in the MR Basin. View Full-Text
Keywords: Mahanadi River; genetic algorithm; suspended sediment yield; artificial neural network; water discharge Mahanadi River; genetic algorithm; suspended sediment yield; artificial neural network; water discharge
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MDPI and ACS Style

Yadav, A.; Hasan, M.K.; Joshi, D.; Kumar, V.; Aman, A.H.M.; Alhumyani, H.; Alzaidi, M.S.; Mishra, H. Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm. Water 2022, 14, 2815. https://doi.org/10.3390/w14182815

AMA Style

Yadav A, Hasan MK, Joshi D, Kumar V, Aman AHM, Alhumyani H, Alzaidi MS, Mishra H. Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm. Water. 2022; 14(18):2815. https://doi.org/10.3390/w14182815

Chicago/Turabian Style

Yadav, Arvind, Mohammad Kamrul Hasan, Devendra Joshi, Vinod Kumar, Azana Hafizah Mohd Aman, Hesham Alhumyani, Mohammed S. Alzaidi, and Haripriya Mishra. 2022. "Optimized Scenario for Estimating Suspended Sediment Yield Using an Artificial Neural Network Coupled with a Genetic Algorithm" Water 14, no. 18: 2815. https://doi.org/10.3390/w14182815

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