Cities in the developed world lose between 10-30% of their drinking water through leaks. Water companies call this “non-revenue water” because they treat the water but cannot charge for it since it doesn’t reach the user.
What water companies need is an efficient system that can collect and parse monitoring data so leaks can be found and repaired.
Israeli startup TaKaDu aims to fill that niche with its water infrastructure monitoring service. UK-based Thames Water, for example, uses TaKaDu to detect leaks up to 9 days earlier than with its previous system.
In the following interview, TaKaDu’s VP of Marketing Guy Horowitz talks about how monitoring systems use data to plug these costly leaks.
How much revenue is lost to leaks?
Guy Horowitz: There are direct losses and indirect losses. Direct losses are the cost of water and energy. In many countries, the water that is lost — desalinated water for example — is very expensive to produce. Even in countries with abundant water, water has to be pumped, moved and stored, which contributes to the cost of water. A city such as London loses 30% of its water; over 600 million cubic meters. A cubic meter can cost anywhere from a $0.20 to a few dollars, so we’re talking about hundreds of thousands of dollars, if not millions, per day. Add to that the energy required to replace the water lost, and you get a significant pain point.
Indirect losses are often higher than direct losses. Road and property damage, traffic interruptions, paying detection crews and repair crews, and in many cases regulatory fines, amount to hundreds of millions per year in large cities. Detection alone is a mega-expenditure.
Additional loss of revenue is associated with sub-optimized maintenance, like fixing the wrong infrastructure or replacing perfectly good pipes because of age and material, though they are not faulty at all.
What types of data are available in a water network?
Guy Horowitz: TaKaDu does not add any new sensors, but uses existing sensors and data. Data from flow and pressure meters, Geographical information system (GIS) data, maintenance records, access control records, quality sensors, and many other types are available.
Water networks can be divided into smaller areas, dubbed district metered areas (DMAs). This approach can be helpful in monitoring water loss, but it is costly to implement and, until systems like TaKaDu, required heavy human interpretation of data. TaKaDu can take a DMA-ed network and automate most of the detection without adding new meters or sensors. Since we detect deviations from normal behavior, we typically ask for a year of history to account for all benign seasonal patterns and holiday exceptions.
What are the main techniques you use to analyze water data?
Guy Horowitz: The full explanation would be lengthy, but one example is cross-site correlation. Your neighborhood and some other neighborhood maintain a consumption relationship that holds true year-long. Why? They may have a similar demographic make-up or similar mix of residential and industrial customers. It doesn’t really matter as long as they demonstrate a similar behavior across time. Now assume your neighborhood’s consumption goes up by 10% while the “similar” neighborhood does not. That may indicate a problem, while if both increase it may indicate a warm day, or some other benign explanation. Other algorithms kick in to check all possible explanations, and if nothing explains the change, it is declared to be a problem and is classified according to its type.
Do you detect other kinds of problems apart from leaks?
Guy Horowitz: Leaks and bursts account for only about a third of the types of problems detected by TaKaDu. A majority of alerts are on inefficiencies and faults in the network setup, operation or transmission, including faulty equipment and incorrect metering. The system also gives alerts on water quality issues, energy inefficiencies and other types of faults.
What’s next for data analytics in water infrastructure management?
Guy Horowitz: We see very high interest from across the industry in smarter data-driven solutions for better management of water networks. I expect larger players to make significant moves in product development and collaboration with innovative smart-water players. The challenges posed by aging infrastructure and the growing investment gap in the water sector means that we need the best brains to start thinking about water problems rather than developing new mobile apps. The coming decade will be the decade of smarter water networks.
This interview was edited and condensed.