%0 Journal Article
%T Monthly Rainfall Estimation Using Data-Mining Process
%A £¿zlem Terzi
%J Applied Computational Intelligence and Soft Computing
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/698071
%X 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, E£¿irdir, and Yalva£¿ 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 E£¿irdir 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. 1. Introduction The meteorological events affect permanently human life. Considering the meteorological phenomena, which have no possibility of intervention, they cause the important results in human life, accurate estimation and analysis of these variables are also very important. Precipitation, which is generating flow, is an important parameter. The occurrence of extreme rainfall in a short time causes significant events that affect human life such as flood. However, in the event of insufficient rainfall in long period occurs drought. Thus, rainfall estimation is very important in terms of effects on human life, water resources, and water usage areas. However, rainfall affected by the geographical and regional variations and features is very difficult to estimate. Nowadays, there are many researches about artificial intelligence methods used in the estimation of rainfall [1¨C7]. Partal et al. [8] developed rainfall estimation models using artificial neural networks and wavelet transform methods. Bodri and £¿erm¨¢k [9] evaluated the applicability of neural networks for precipitation prediction. Chang et al. [10] applied a modified method, combining the inverse distance method and fuzzy theory, to precipitation interpolation. They used genetic algorithm to determine the parameters of fuzzy membership functions, which represent the relationship between the location without rainfall records and its surrounding rainfall gauges. They worked to minimize the estimated error of precipitation with the optimization process. One of the aims of storing this data in databases and receiving data from many sources is to convert raw data into information at present. This process is called as data-mining (DM) process of converting data into information. In
%U http://www.hindawi.com/journals/acisc/2012/698071/