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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 698071, 6 pages
http://dx.doi.org/10.1155/2012/698071
Research Article

Monthly Rainfall Estimation Using Data-Mining Process

Faculty of Technical Education, Suleyman Demirel University, 32260 Isparta, Turkey

Received 16 April 2012; Revised 13 July 2012; Accepted 18 July 2012

Academic Editor: Tzung P. Hong

Copyright © 2012 Özlem Terzi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

It is important to accurately estimate rainfall for effective use of water resources and optimal planning of water structures. For this purpose, the models were developed to estimate rainfall in Isparta using the data-mining process. The different input combinations having 1-, 2-, 3- and 4-input parameters were tried using the rainfall values of Senirkent, Uluborlu, Eğirdir, and Yalvaç stations in Isparta. The most appropriate algorithm was determined as multilinear regression among the models developed with various data-mining algorithms. The input parameters of Multilinear Regression model were the monthly rainfall values of Senirkent, Uluborlu and Eğirdir stations. The relative error of this model was calculated as 0.7%. It was shown that the data mining process can be used in estimation of missing rainfall values.