Application of Hybrid Neural Modeling and Radial Basis Function Neural Network to Estimate Leakage Rate in Water Distribution Network

In this research the ability of a hybrid model of artificial neural network (ANN), feed forward networks (FFN) and recurrent neural networks (RNN), is investigated with genetic algorithm (GA). GA is used in order to determine the optimal structure of ANN (i.e. the number of neurons for each hidden layer). Furthermore, hybrid model's results are compared with radial basis function neural network (RBF). A water supply network located in Kerman, Iran is considered as case study in order to illustrate the efficiency of the modeling procedure. Obtained results apparently show that the ANN-GA models can be used successfully to estimate leakage rate in water distribution networks. In addition, a comparative study of models indicates that the feed forward networks hybrid with GA performed better than the other models.

[1]  Valentin Jijkoun,et al.  The University of Amsterdam at WiQA 2006 , 2006, CLEF.

[2]  Ali Zilouchian,et al.  FUNDAMENTALS OF NEURAL NETWORKS , 2001 .

[3]  Joby Boxall,et al.  ONLINE APPLICATION OF ANN AND FUZZY LOGIC SYSTEM FOR BURST DETECTION , 2009 .

[4]  Mohammad Karamouz,et al.  Pressure Management Model for Urban Water Distribution Networks , 2010 .

[5]  Fernando Alvarruiz,et al.  Use of an artificial neural network to capture the domain knowledge of a conventional hydraulic simulation model , 2007 .

[6]  M. S. Mohan Kumar,et al.  Prediction of multi-components (chlorine, biomass and substrate concentrations) in water distribution systems using artificial neural network (ANN) models , 2009 .

[7]  S. Alvisi,et al.  Optimizing the operation of the Valencia water-distribution network , 2006 .

[8]  I. Annus,et al.  Use of Pressure Dynamics for Calibration of Water Distribution System and Leakage Detection , 2009 .

[9]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[10]  D. Hammerstrom,et al.  Working with neural networks , 1993, IEEE Spectrum.

[11]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[12]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[13]  Scott Robert Ladd,et al.  Genetic algorithms in C , 1995 .

[14]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[15]  Holger R. Maier,et al.  Forecasting chlorine residuals in a water distribution system using a general regression neural network , 2006, Math. Comput. Model..

[16]  Elad Salomons,et al.  Optimizing the operation of the Haifa-A water-distribution network , 2007 .

[17]  Gail D. Baura,et al.  Nonlinear System Identification , 2002 .

[18]  Stephen R. Mounce,et al.  Burst detection using hydraulic data from water distribution systems with artificial neural networks , 2006 .

[19]  R. Tabatabaei.  Estimation of Failure Probability in Water Pipes Network Using Statistical Model , 2010 .