Applied mathematics in water supply network management
Automatica (Journal of IFAC) - IFAC-IEEE special issue on meeting the challenge of computer science in the industrial applications of control
Agglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network
Journal of VLSI Signal Processing Systems
NSS '09 Proceedings of the 2009 Third International Conference on Network and System Security
Algorithm of pipeline leak detection based on discrete incremental clustering method
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
EMS '11 Proceedings of the 2011 UKSim 5th European Symposium on Computer Modeling and Simulation
ISMS '12 Proceedings of the 2012 Third International Conference on Intelligent Systems Modelling and Simulation
Fault detection in water supply systems using hybrid (theory and data-driven) modelling
Mathematical and Computer Modelling: An International Journal
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
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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 24h 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 produces better recognition rates in comparison to the training based on patterns of nodal heads and pipe flows state estimates with or without confidence limits. It produces also comparable recognition rates to the original recognition system trained with patterns of data obtained with the LS nodal heads state estimator while being computationally superior by requiring a single architecture of the GFMMNN type and using a small number of pattern recognition hyperbox fuzzy sets built by the same GFMMNN architecture. In this case the GFMMNN relies on the ability of the LS loop flows state estimator of making full use of the pressure/nodal heads measurements existent in a water network.