Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm
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RESEARCH ARTICLE
Suspended sediment load prediction using artificial neural network and ant lion optimization algorithm Fatemeh Barzegari Banadkooki 1 & Mohammad Ehteram 2 & Ali Najah Ahmed 3 & Fang Yenn Teo 4 & Mahboube Ebrahimi 1 & Chow Ming Fai 5 & Yuk Feng Huang 6 & Ahmed El-Shafie 7,8 Received: 2 January 2020 / Accepted: 23 June 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Suspended sediment load (SSL) estimation is a required exercise in water resource management. This article proposes the use of hybrid artificial neural network (ANN) models, for the prediction of SSL, based on previous SSL values. Different input scenarios of daily SSL were used to evaluate the capacity of the ANN-ant lion optimization (ALO), ANN-bat algorithm (BA) and ANN-particle swarm optimization (PSO). The Goorganrood basin in Iran was selected for this study. First, the lagged SSL data were used as the inputs to the models. Next, the rainfall and temperature data were used. Optimization algorithms were used to fine-tune the parameters of the ANN model. Three statistical indexes were used to evaluate the accuracy of the models: the root-mean-square error (RMSE), mean absolute error (MAE) and Nash-Sutcliffe efficiency (NSE). An uncertainty analysis of the predicting models was performed to evaluate the capability of the hybrid ANN models. A comparison of models indicated that the ANN-ALO improved the RMSE accuracy of the ANN-BA and ANN-PSO models by 18% and 26%, respectively. Based on the uncertainty analysis, it can be surmised that the ANN-ALO has an acceptable degree of uncertainty in predicting daily SSL. Generally, the results indicate that the ANN-ALO is applicable for a variety of water resource management operations. Keywords River suspended sediment load . Artificial neural network . Ant lion optimization . Bat algorithm . Particle swarm optimization . Sensitivity analysis
Responsible editor: Marcus Schulz * Ali Najah Ahmed [email protected]
1
Agricultural Department, Payam Noor University, Tehran, Iran
2
Fatemeh Barzegari Banadkooki [email protected]
Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
3
Mohammad Ehteram [email protected]
Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor, Malaysia
4
Fang Yenn Teo [email protected]
Faculty of Science and Engineering, University of Nottingham Malaysia, 43500 Semenyih, Selangor, Malaysia
5
Mahboube Ebrahimi [email protected]
Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), 43000 Kajang, Selangor, Malaysia
6
Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43200, Kajang, Selangor, Malaysia
Yuk Feng Huang [email protected]
7
Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia
Ahmed El-Shafie [email protected]
8
National Water
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