Many utilities are “data rich” but “information poor.” Or, stated differently, utilities, and industry in general, find themselves collecting rivers of data, while very little synthesis of the data is performed. Without data synthesis, information necessary to enhance decision-making and improve processes is not produced. However, with the newer information technologies presently available, such as data mining, this need not be the case. Most utilities already have a wealth of data ready to be transformed into a rich source of information. Data mining has been defined as “the search for valuable information in large volumes of data. It is a cooperative effort of humans and computers. (Weiss and Indurkhya, 1998). The purpose of this paper is to describe some of the techniques which are available to extract information from databases and to demonstrate how large amounts of data have been evaluated using sophisticated data mining techniques that have enabled utility managers and other decision makers to make informed decisions which have more robust and less risky. Various practical case studies will also be offered for illustration.
Data mining, artificial neural networks, water resources, TMDL, demand forecasting