A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads

Haitham Abdulmohsin Afan (), Wan Hanna Melini Wan Mohtar, Muammer Aksoy, Ali Najah Ahmed, Faidhalrahman Khaleel, Md Munir Hayet Khan, Ammar Hatem Kamel, Mohsen Sherif and Ahmed El-Shafie
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Haitham Abdulmohsin Afan: University of Anbar
Wan Hanna Melini Wan Mohtar: Universiti Kebangsaan Malaysia, UKM
Muammer Aksoy: Al-Mustaqbal University
Ali Najah Ahmed: Sunway University
Faidhalrahman Khaleel: University of Al Maarif
Md Munir Hayet Khan: INTI International University (INTI-IU), Persiaran Perdana BBN
Ammar Hatem Kamel: University of Anbar
Mohsen Sherif: National Water and Energy Center, United Arab Emirates University
Ahmed El-Shafie: National Water and Energy Center, United Arab Emirates University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 5, No 5, 2033-2048

Abstract: Abstract The ability to accurately predict suspended sediment load (SSL) in a river is vital for various stakeholders. Predicting SSL can help inform efforts to reduce the negative impacts of floods and droughts and help inform mitigation efforts for extreme environmental events that have a significant impact on the availability of clean water. In this regard, this study proposes a multifunctional Genetic Algorithm-Neural Network (GA-NN) model to predict the SSL using flow discharge and SSL data at Johor River. Furthermore, a comparison study was conducted between the results obtained with the proposed model and with traditional input selection, as well as another optimization method (GHS algorithm). The findings of this study indicate that the GA-NN model is a proficient instrument for forecasting Suspended Sediment Load (SSL) utilizing river discharge and sediment load data from the Johor River. Furthermore, a superior improvement in prediction accuracy was achieved using the GA algorithm, compared to the traditional input selection and GHS algorithm. Based on several statistical matrices and graphical appraisals, the optimum results were achieved within five inputs by providing low margins of errors in terms of Mean Absolute Error (MAE) of 14.366 and Root Mean Square Error (RMSE) of 24.560 and higher correlation accuracy in terms of coefficient of determination (R2) of 0.911. Thus, the Genetic Algorithm (GA) proved its ability to select input patterns, which is considered a critical step in modeling, as it helps to simplify the process of finding the optimal solution to obtain more accurate predictions.

Keywords: Sediment Load Prediction; Genetic Algorithm; Neural Network; Johor River; Artificial Intelligence (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-024-04054-w

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