Volume 11, Issue 3 (2009)                   JAST 2009, 11(3): 335-362 | Back to browse issues page

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Yazdani M, Saghafian B, Mahdian2 M H, Soltani S. Monthly Runoff Estimation Using Artificial Neural Networks. JAST. 11 (3) :335-362
URL: http://journals.modares.ac.ir/article-23-1879-en.html
1- Department of Climatology, College of Literature, University of Isfahan, Isfahan, Islamic Republic of Iran.
2- Soil Conservation and Watershed Management Research Center, Tehran, Islamic Republic of Iran.
3- Department of Watershed Management, College of Natural Resources, Isfahan University of Technology, Isfahan, Islamic Republic of Iran.
Abstract:   (4887 Views)
Runoff estimation is one of the main challenges encountered in water and watershed management. Spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. Artificial Neural Network (ANN) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. These networks can recognize the relation between input and output. In this study ANN model was employed for runoff estimation in Plaszjan Riv-er basin in the central part of Iran. The models used are Multiple Perceptron (MLP) and Recurrent Neural Network (RNN). Inputs include data obtained from 5 rain gauges as well as from 2 temperature recording gauges, the output of the model being the monthly flow in Eskandari Hydrometric Station. Preprocessing of the data as well as the sensitivity analysis of the model were carried out. Different topologies of Neural Networks were cre-ated with change in input layers, nodes as well as in the hidden layer. The best architec-ture was found as 7.4.1. Recurrent Neural Network led to better results than Multilayer Perceptron Network. Also results indicated that ANN is an appropriate technique for monthly runoff estimation in the selected basin with these networks being also of the ca-pability to show basin response to rainfall events.
Full-Text [PDF 295 kb]   (4003 Downloads)    
Subject: Range Science
Received: 1970/01/1 | Accepted: 1970/01/1 | Published: 2009/07/26

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