Evaluating Soft Computing Models for Hydrologically Homogenizing Regional Floods (Case study: Karkheh river basin)

Identification of homologous hydrological groups is one of the fundamental topics in hydrology in both applied and research dimensions. One of the common methods to achieve homogeneous hydrological zones for estimating flood zones is the use of clustering methods. In this study, the using, evaluation and comparison of statistical methods and methods based on artificial intelligence for clustering Karkheh catchment have been investigated. SOM, K-meansand hierarchical clustering were used for the study area. In the following, the study of hydrological homogeneity of the obtained areas was evaluated using the linear torque method and the heterogeneity adjustment was evaluated using the methods proposed by Husking and Wallis. The results show that the study area can be converted into two clusters. The values ​​of homogeneity statistics for the first and second clusters were calculated to be 0.33 and 0.17, respectively, which indicates the homogeneity of each region. Due to the short statistical period in some stations, statistical shortcomings and the reliability of the results of regional frequency analysis, this method should be used in estimating floods in other catchments.