Asking an eye in the sky how much water your yard requires

(credit: Don DeBold)

Few environmental limits are as obvious to people today as water availability. Particularly in drier climates, availability can be a pretty unforgiving equation. Even there, a family might pay less for water than for cell phones, but there is often a pretty complex system behind your tap that keeps it running.

The challenge of water availability rises beyond engineering. It becomes a delicate dance managing demand, forecasting supply, and sustaining ecosystems. Decisions have to be made based on information that is never complete, so any opportunity to obtain more useful information is liable to get a thirsty look from water managers.

Of course, a truck load of information won’t do you any good if you can’t extract the bits you need. One tool for working with potentially valuable truck loads is an artificial neural network—a software system that uses machine learning techniques to process tons of data and intelligently answer questions. And one company is now applying IBM’s Watson machine learning system in an interesting way to tell water utilities something they would love to know: how efficiently their customers are using water.

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