Regionalization of rainfall intensity–duration–frequency (IDF) curves with L-moments method using neural gas networks

Abstract

Floods are one of the most frequent and destructive natural events which lead to lots of human and financial losses with damage to the houses, farms, roads, and other buildings. Intensity–duration–frequency (IDF) curves are the main and practical tools that have been used for flood control studies, including the design of the water structures. In many cases, there is no measuring device at the desired place, or their information is not helpful if there is any available. In this case, it is not possible to extract these curves through conventional methods. Regionalizing the IDF curves is a method that has solved the issues mentioned in the common methods. In this research, the regionalized IDF curves are extracted in Khuzestan province, Iran using 21 rain gauge stations through L-moments and neural gas networks. Clustering is one of the most effective steps and a prerequisite for regional frequency analysis (RFA) that divides the region and existing stations into hydrologically homogenous regions. In this study, clustering is done using two new models named neural gas (NG) and growing neural gas (GNG) network. Comparing the regional IDF curves with at-site curves, it was found that neural gas network models had a more accurate performance and higher efficiency, so they had the lowest estimate error amount among other models. Also, due to the acceptable difference between regional and at-site curves, the efficiency of L-moments in RFA was evaluated as appropriate.