Volume 13, Issue 50 (winter 2010)                   jwss 2010, 13(50): 29-40 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Tabari H, Marofi S, Zare Abiane H, Amiri Chayjan R, Sharifi M, Akhondali A. Comparison of Non-Linear Regression and Computational Intelligence Methods in Estimating Spatial Distribution of Snow Water Equivalent in Karoon Upstream. jwss 2010; 13 (50) :29-40
URL: http://jstnar.iut.ac.ir/article-1-1184-en.html
, smarofi@yahoo.com
Abstract:   (31810 Views)
In mountainous basins, snow water equivalent is usually used to evaluate water resources related to snow. In this research, based on the observed data, the snow depth and its water equivalent was studied through application of non-linear regression, artificial neural network as well as optimization of network's parameters with genetic algorithm. To this end, the estimated values by artificial neural network, neural network-genetic algorithm combined method and regression method were compared with the observed data. The field measurement were carried out in the Samsami basin in February 2006. Correlation coefficient (r) mean square error (MSE) and mean absolute error (MAE) were used to evaluate efficiency of the various models of artificial neural networks and nonlinear regression models. The results showed that artificial neural network and genetic algorithm combined methods were suitable to estimate snow water equivalent. In general, among the methods used, neural network-genetic algorithm combined method presented the best result (r= 0.84, MSE= 0.041 and MAE= 0.051). Of the parameters considered, elevation from sea level is the most important and effective to estimate snow water equivalent.
Full-Text [PDF 421 kb]   (5349 Downloads)    
Type of Study: Research | Subject: Ggeneral
Received: 2010/08/22 | Published: 2010/01/15

Add your comments about this article : Your username or Email:
CAPTCHA

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb