Arabian Journal of Geosciences, ( ISI ), Volume (9), No (13), Year (2016-8) , Pages (1-9)

Title : ( Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study )

Authors: , Saeed Reza Khodashenas , Kamran Davary , Mohammad Mousavi Baygi , Fatemeh Karimaldini ,

Access to full-text not allowed by authors

Citation: BibTeX | EndNote

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M , have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.

Keywords

, Artificial neural networks.Mashhad synoptic, station.Rainfall
برای دانلود از شناسه و رمز عبور پرتال پویا استفاده کنید.

@article{paperid:1057920,
author = {, and Khodashenas, Saeed Reza and Davary, Kamran and Mousavi Baygi, Mohammad and Fatemeh Karimaldini},
title = {Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study},
journal = {Arabian Journal of Geosciences},
year = {2016},
volume = {9},
number = {13},
month = {August},
issn = {1866-7511},
pages = {1--9},
numpages = {8},
keywords = {Artificial neural networks.Mashhad synoptic;station.Rainfall prediction},
}

[Download]

%0 Journal Article
%T Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study
%A ,
%A Khodashenas, Saeed Reza
%A Davary, Kamran
%A Mousavi Baygi, Mohammad
%A Fatemeh Karimaldini
%J Arabian Journal of Geosciences
%@ 1866-7511
%D 2016

[Download]