Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm
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Original Paper
Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm Fatemeh Barzegari Banadkooki,1,9 Mohammad Ehteram,2 Ali Najah Ahmed,3 Fang Yenn Teo,4 Chow Ming Fai,5 Haitham Abdulmohsin Afan ,6,9 Michelle Sapitang,7 and Ahmed El-Shafie8 Received 30 September 2019; accepted 7 February 2020
The present study attempted to predict groundwater levels (GWL) obtained from precipitation and temperature data based on various temporal delays. The radial basis function (RBF) neural network–whale algorithm (WA) model, the multilayer perception (MLP– WA) model, and genetic programming (GP) were used to predict GWL. The objectives were: (1) to prepare robust hybrid ANN models; (2) to study the combination of ANN models and optimization algorithms; and (3) to study uncertainty related to the input parameters of the models, whereby three scenarios with different inputs were considered. The results showed that for the first scenario, in which the input data were just the average of the region temperature and three temporal delays of 3, 6, and 9 months were considered, the models based on the three simultaneous temperature inputs with mentioned delays had higher performance as compared to the inputs just belonging to temperature input. The MLP–WA model was the best model among all. For the test stage, the mean absolute error of the MLP–WA model decreased to 30% and from 31 to 38% as compared to the radial basis function–whale algorithm (RBF–WA) and GP models, respectively. The second scenario was the evaluation of the predicted GWL based on the precipitation data of 3, 6, and 9 months. The results showed that the three variations of precipitation data as simultaneous input improved the models performance. The third scenario was considered in which the data from average precipitation and temperature were simultaneously used. The best results were obtained when the precipitation and temperature data with delays of 3, 6, and 9 months were used as input. KEY WORDS: Groundwater, Water resources management, Whale algorithm, Neural network.
1
Agricultural Department, Payam Noor University, Tehran, Iran. Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 3513119111, Iran. 3 Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000 Selangor, Malaysia. 4 Faculty of Science and Engineering, University of Nottingham Malaysia, 43500 Semenyih, Selangor, Malaysia. 5 Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional (UNITEN), 43000 Selangor, Malaysia. 2
6
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam. 7 Department of Civil Engineering, Universiti Tenaga Nasional (UNITEN), 43000 Selangor, Malaysia. 8 Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia. 9 To whom correspondence should be addressed; e-mail: [email protected], [email protected]
2020 International Ass
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