Extracting physical homogeneous regions out of irrigation networks using fuzzy clustering method: a case study for the Ghazvin canal irrigation network

Abstract

Improving the current operation and maintenance activities is one of the main steps in achieving higher performance of irrigation networks. Improving the irrigation network management, influenced by different spatial and temporal parameters, is confronted with special difficulties. One of the controversial issues often faced by decision-makers is how to cope with the spatial diversity of irrigation systems. Homogeneous area detection out of the irrigation networks could improve the current management of networks. The idea behind this research is to present a quantitative benchmark for exploring the homogeneous areas with similar physical attributes out of the network region. Five physical attributes, such as length, capacity, number of intakes, number of conveyance structures and the covered irrigated area for each canal reach, are used for spatial clustering. Two fuzzy clustering algorithms, namely FCM and GK, are applied to the Ghazvin irrigation network. Using a clustering validity index, SC, shows that the GK algorithm is the more appropriate tool for clustering of the considered dataset. According to the results the optimal number of clusters for the Ghazvin irrigation project is derived as nine clusters and the irrigated district is classified into nine homogeneous areas. Physical homogeneous regions provide a context for better and easier decision-making.