Applying Knowledge Discovery to Predict Water-Supply Consumption

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 minimumcost 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.