Sofichay River Runoff Modeling using Support Vector Machine and Artificial Neural Network

akhoni pourhosseini, fatemeh; darbandi, sabereh

doi:10.29252/jwmr.9.17.57

Volume 9, Issue 17 (9-2018) J Watershed Manage Res 2018, 9(17): 57-66 | Back to browse issues page

‎ 10.29252/jwmr.9.17.57

‎ 20.1001.1.22516174.1397.9.17.2.4

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akhoni pourhosseini F, darbandi S. (2018). Sofichay River Runoff Modeling using Support Vector Machine and Artificial Neural Network. J Watershed Manage Res. 9(17), 57-66. doi:10.29252/jwmr.9.17.57
URL: http://jwmr.sanru.ac.ir/article-1-610-en.html

Sofichay River Runoff Modeling using Support Vector Machine and Artificial Neural Network

Fatemeh Akhoni pourhosseini ^*¹, Sabereh Darbandi²

1- university of tabriz
2- sdarbandi.tabrizu@yahoo.com

Abstract: (3302 Views)

Accurate simulation runoff process can have a significant role in water resources management and related issues. The inherent complexity of this process makes difficult the use of physical and numerical models. In recent years, application of intelligent models is increased a powerful tool in hydrological modeling. The aim of this study was the application of the Gamma test to select the optimal combination of input variables for runoff modeling in Sofi Chay. Streamflow modeling was performed based on the optimum number of the selected variables using the artificial neural network (ANN) and Support vector machine (SVM) methods .Gamma test results showed that monthly runoff with six antecedent runoff values provide better results to predict. Runoff simulation using support vector machines and artificial neural network models also showed that the best input structure will be delayed until six to predict of next month runoff. Among to models with the same input structure, support vector machine have relatively high efficiency compared to artificial neural network .

Keywords: Artificial Neural Network, Gamma Test, Runoff, SofiChay River, Support Vector Machine

Full-Text [PDF 4525 kb] (1100 Downloads)

Type of Study: Research | Subject: Special
Received: 2016/04/21 | Accepted: 2016/09/4

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