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Simulation of the rainfall-runoff process in the drainage basin of Aharchay using data mining techniques
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Ali Ebrahimzadeh *   1, Bita Bagheri2 , Vahid Nourani3 |
1- Islamic Azad University
2- Department of Civil Engineering, Islamic Azad University of Marand, Marand, Iran
3- University of Tabriz |
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Abstract: (4486 Views) |
| The simulation of the rainfall-runoff process is a crucial step in water resources management, watershed management, water scarcity crisis, and flood control. The intrinsic complexity of the rainfall-runoff process, spatiotemporal variability, and the factors affecting it make the simulation with physical or hydrological models difficult. Therefore, metaheuristic approaches, such as support vector machines, gene expression programming, and artificial neural networks, have been widely used in hydrological studies, and generally, in the phenomena without definite relationships. Due to the provision of drinking, agricultural, and industrial water, the drainage basin of Aharchay, located in the northwest of Iran, has an influential role in the development of the region. This paper has evaluated the models of support vector machine, gene expression programming, and artificial neural networks for the simulation of the rainfall-runoff process in the drainage basin of Aharchay at the hydrometric stations of Tazeh Kand, Ravasjan, Oushdilaq, Barmis, Owrang, and Kasin. In order to determine the input combination of the models, a list of independent variables associated with the runoff of each station was prepared. Then the appropriate inputs were chosen using the two criteria of the Pearson correlation coefficient and partial mutual information. The input combinations obtained from each criterion were evaluated in the simulation of the rainfall-runoff of the Aharchay drainage basin in the hydrometer stations. The results indicated the reasonable accuracy of the models of support vector machine and gene expression programming, and the relative superiority of the artificial neural network. Moreover, in determining the input variables, the Pearson correlation coefficient provided the best results or was close to them. |
Article number: 5 |
| Keywords: Artificial neural network, Gene expression programming, Hydrometric station, Support vector machine |
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Full-Text [PDF 1362 kb]
(118 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2020/10/29 | Accepted: 2021/06/24 | Published: 2023/10/2
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