Classification of evaporation stations using fuzzy cluster analysis and Kohonen artificial neural networks

Evaporation is the important factor that affects temperature, drought severity and water storage in the hydrological cycle and plays an important role on managing the water resources projects such as agricultural irrigation.
Classification of datasets is useful for concisely system modeling purposes. By classification, a large number of datasets is reduced to a small number of groups. In the field of hydrological systems, classification of meteorological stations into homogeneous groups will be useful to consider a different scale of measure, which is suitable to each group. Such classification can lead to choice methods appropriate for each group for management of water resources in various regions. Classification will also be useful for prediction of events such as droughts. Moreover, in the case of estimating missing data, the corresponding data of the representative station determined using a classification technique can be successfully substituted (Raju and Kumar 2007).
stations. Dikbas et al. (2011) applied the FCM method to classify the precipitation series and identify the hydrologically homogeneous groups in Turkish. Regional homogeneity test results showed that regions determined by the FCM approach are sufficiently homogeneous for regional frequency analysis.
In the present study, the practical applicability of two classification methods, namely fuzzy c-means (FCM) cluster analysis and Kohonen artificial neural networks (KANN), is examined for grouping 97 evaporation stations in Iran into homogeneous groups. The rest of the paper is organized as follows. First, a description of the case study is presented. After introducing the applied methods, results obtained are presented and discussed and conclusion drawn.