A Probabilistic Estimation Model for the Recoverable Leakage of the Water Distribution Network

Abstract

Water Distribution Networks (WDN) play a vitally important role in preserving and providing a desirable life quality to the public. A WDN should provide, during its economic life, the required quality and quantity of water at required pressures.

Leakage rate and its high associated cost of failure have reached a level that now draws the attention of both policy and decision makers. Leakage is usually the major cause of water loss in water distribution systems. EPA reported in 2007, 240,000 water main breaks per year in the US. The USGS in 2007 estimated that water lost from water distribution systems is 1.7 trillion gallons per year at a national cost of $2.6 billion per year.

Leakage occurs in different components of the water distribution system. Causes of leaks include corrosion, soil corrosivity, excessive water pressure, material defects, water hammer, excessive loads and vibration from road traffic and stray electric current.

In this dissertation a probabilistic estimation model for the recoverable leakage of WDNs was presented factoring key causes that lead to high percentages of leakage in different components of the WDN. The model receives the deterministic and stochastic description of the leakage of the WDN received from the research survey. It is evident that IWA's model for estimating Unavoidable Annual Real Losses (UARL) does not account for soil corrosivity. The UARL equation can be modified by adding a new soil corrosivity factor (Cr) that takes the soil corrosivity into consideration.

Linear Regression was used to develop a relationship between the UARL and the soil corrosivity. Directional cosines analysis examined the importance of the random variables in the new probabilistic estimation model. Two Case Studies were used to validate the modified formulation for the UARL using the data for the leakage component parameters and the system water audit.

Monte Carlo simulation was operated twice till the distribution had minimal change. After adding the Cr the output distributions for the UARL had a 43% decrease in the standard deviation value which shows that the corrosion behavior of WDNs is closely related to the environmental factors.