Study On Leakage Control Of Municipal Water Supply Network

In this paper, leakage control in municipal water supply network was mainly studied. The models of water loss as well as the models of leakage detection and location were established by hydraulic analysis under simulation lab-scale environment which can provide theory to reduce water loss effectively,the existing models were verified simultaneously;Based on date mining, the macro factors and law of leakage was disserted and the prediction model was presented which can provide technical support for taking initiative control of leakage,policy decision to prevention,maintenance,renewal and rational use of renewal fund.First of all, leakage on pipes and bursts at nodes were simulated and the pressure loss as well as flow changes of pipe and consumer caused by single,two leakages and bursts was studied which was the basis of preliminary location.The pressure loss caused by leakages and bursts was simulated by EPANET The leakage at the same time.Secondly, approaches to identify the location and severity based on the pressure and flow rate at some monitored points were developed based on Genetic Algorithm and BP neural network , Bayesian probabilistic framework as well as R. A. Fisher theory, Cluster Observations Discrimination Analysis theory which can lay a theory foundation to real-time troubleshooting and reduce blindness to maintenance of water distribution network.Further more, the remarkable factors were found after principal components analysis and stepwise regression to hydraulic factors influencing water loss on pipe and the models were built; Index section of pressure was drawn up based on analysis to relationship between the amount of leakage and pressure in different leakage area; The existing models were verified by means of estimate the leakage value and section value of coefficients.Lastly, The leakage and burst number was studied statistically.Following this, the leakage time prediction models of RBF neural network (laid time<5year) and multi-linear regression model (laid time>5year) were found, The time alignment prediction model of ARIMA ( p ,d,q)(P,D,Q)sand ELMAN feedback neural network as well as Ordinal Logistic Regression model to leakage point were established too.