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Applying Knowledge Discovery to Predict Water-Supply Consumption
July-August 1997 (vol. 12 no. 4)
pp. 72-78
Optimizing control of operations in a municipal water-distribution system can reduce electricity costs and realize other economic benefits. However, optimal control requires an ability to precisely predict short-term water demand so that minimum-cost pumping schedules can be prepared. One of the objectives of our project to develop an intelligent system for monitoring and controlling municipal water-supply systems is to ensure optimal control and reduce energy costs. Hence, prediction of water demand is essential. In this article, we present an application of a rough-set approach for automated discovery of rules from a set of data samples for daily water-demand predictions. The database contains 306 training samples, covering information on seven environmental and sociological factors and their corresponding daily volume of distribution flow.
Citation:
Aijun An, Christine Chan, Ning Shan, Nick Cercone, Wojciech Ziarko, "Applying Knowledge Discovery to Predict Water-Supply Consumption," IEEE Intelligent Systems, vol. 12, no. 4, pp. 72-78, July-Aug. 1997, doi:10.1109/64.608199
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