Decision Support System for Water Distribution Systems Based on Neural Networks and Graphs

This paper presents an efficient and effective Decision Support System (DSS) for operational monitoring and control of water distribution systems based on a three layer General Fuzzy Min-Max Neural Network (GFMMNN) and graph theory. The operational monitoring and control involves detection of pipe leakages. The training data for the GFMMNN is obtained through simulation of leakages in a water network for a given operational period. The training data generation scheme includes a simulator algorithm based on loop corrective flows equations, a Least Squares (LS) loop flows state estimator and a Confidence Limit Analysis (CLA) algorithm for uncertainty quantification entitled Error Maximization (EM) algorithm. These three numerical algorithms for modeling and simulation of water networks are based on loop corrective flows equations and graph theory. It is shown that the detection of leakages based on the training and testing of the GFMMNN with patterns of variation of nodal consumptions with or without confidence limits is computational superior to the training based on patterns of nodal heads and pipe flows state estimates with or without confidence limits and to the original recognition system trained with patterns of data obtained with the LS nodal heads state estimator.