منطقه بندی حوزه های آبخیز با به کارگیری نوعی از شبکه های عصبی مصنوعی به منظور تحلیل فراوانی منطقه ای سیلاب

Self-Organizing Feature Maps (SOFM) are a variety of artificial neural networks that their applications in the areas of pattern recognition and data clustering makes them noticeable tools to perform regional flood frequency analysis (RFFA). In this study, ability of Self-Organizing Feature Maps for regionalization of Sefidrood watershed in order to perform regional flood frequency analysis using L-moment algorithm is assessed. Results of this study show that SOFMs may be used as an acceptable method for data clustering and regionalization of watersheds. Evaluation of values of cluster validity measures showed that they can’t be a determining factor to identify suitable number of regions for regional flood frequency analysis, but homogeneity of regions is main factor to determine desirable number of regions. According to homogeneity of regions and sizes of formed regions, regionalizations including two regions that formed by Ward’s algorithm and SOFM were chosen as optimum choices to regional flood frequency analysis on Sefidrood watershed. Furthermore, based on results of flood estimation by at-site FFA and two RFFA, regional estimates are very close to each other and their average relative difference is equal to 1% nearly. Also relative difference between regional and at-site estimates doesn’t exceed 17% in any station and its mean value is about 8%.