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Journal of Environmental Hydrology
ISSN 1058-3912 |
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Electronic Journal of the International Association for
Environmental Hydrology
JEH Volume 19 (2011), Paper 5 Posted February 28, 2011 NEURAL NETWORK APPLICATION FOR MONTHLY PRECIPITATION DATA RECONSTRUCTION
Zohre Khorsandi1 2Department of Natural Resources, Tehran University, Karaj, Iran 3Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Iran ABSTRACT The need for precipitation data and the importance of its duration in hydrological and climatic phenomena motivate investigators to develop reconstruction methods. Four methods are artificial neural network, normal ratio, inverse distance weighting, and geographical coordinate. These methods are compared in this study using monthly precipitation data of three stations, Dolat-Abad, Kabootar-Abad and Refinery plant around the city of Esfahan, Iran. Mean absolute error and coefficient of correlation of the results are compared to select the most appropriate method. The neural network approach shows the best performance compared with the other methods. Reference: Khorsandi, Z., M. Mahdavi, A. Salajeghe, and S. Eslamian. 2011. Neural network application for monthly precipitation data reconstruction. Journal of Environmental Hydrology, Vol. 19, Paper 5. CONTACT: Zohre Khorsandi Department of Natural Resources Science and Research Branch Islamic Azad University Tehran, Iran E-mail: khorsandi_zohra@yahoo.com |
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