“ANY industry that mislays 25-30% of its product in the process of delivering it might reasonably be thought to have a problem. Yet that, according to the World Bank, is the case for the world's water companies. Though water is cheap, it is not free. According to a report published by the Bank in 2006, leaks even then were costing $14 billion a year. But to plug a leak you have to find it. Water mains are hard to inspect, particularly if they are underground. Many are old and thus decrepit. And outright theft is not unheard of, as the poor seek to fill their drinking vessels and the rich their swimming pools. An effective way of detecting leaks, both accidental and deliberate, would therefore be welcome.
TaKaDu, a firm based near Tel Aviv, thinks it has one.”
“TaKaDu's engineers have therefore developed a monitoring system called a statistical anomaly detection engine that is intended to identify clues in the data which might otherwise be missed. It applies a range of statistical tests (linear-regression analysis is one of the more familiar) to the data stream, and thus works out when the incoming signals are deviating significantly from normal behavior. Sometimes such deviations are caused by faulty meters. Sometimes they are caused by leaks. Either way, that is valuable knowledge.
To know what is deviant you have, of course, to know what is normal. Even a 1% change in flow rate can sometimes be significant, if it is persistent, but that is not always the case. Existing leak-detection systems therefore have thresholds built into them, to avoid false alarms. The price of this is that small leaks may go undetected and thus unrepaired, which often leads to larger leaks later. The detection engine attempts to work out what is important by using a process of continuous modeling to define normality. This identifies both obvious patterns—such as daily, weekly and annual flow-rates—and subtle ones, such as correlations between the behaviours of widely separated parts of the system that are brought about by things like similarities in network layout or in the behaviour of local customers.”