Development of the generalized structure of the group method of data handling (GSGMDH) method based on multiple output layers to predict multiple leaks in urban water systems

ABSTRACT In this research, a developed method according to the generalized structure of the group method of data handling (GSGMDH) method is used in comparison with the SAELM method to find the location and amount of leakage in a water distribution network. In this method, some limitations of using artificial intelligence techniques, including the number of output layers of machine-learning models, are removed so that the model is able to predict several outputs at the same time. Also, the implementation process, which is relatively time-consuming in most of these techniques when dealing with large water distribution networks, has been significantly reduced in the GSGMDH model. These methods are implemented on a sample circular water distribution network whose data are available. The results indicate that the developed method, in which the nature of the dependence of the leakage rate on the pressure is preserved, is able to identify the position of the leaking nodes and also accurately predict the location and the amount of leakage in the node that has less leakage, with minimal extraction of hydraulic information of the nodal pressure type. This method can replace many expensive and time-consuming conventional methods as well as tools and hardware in urban water distribution networks.