Discovering rules for water demand prediction: An enhanced rough-set approach
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TL;DR
An enhanced rough-sets method for generating prediction rules from a set of observed data is presented, extending upon the standard rough set model by making use of the statistical information inherent in the data to handle incomplete and ambiguous training samples.
Abstract
Prediction of consumer demands is a pre-requisite for optimal control of water distribution systems because minimum-cost pumping schedules can be computed if water demands are accurately estimated. This paper presents an enhanced rough-sets method for generating prediction rules from a set of observed data. The proposed method extends upon the standard rough set model by making use of the statistical information inherent in the data to handle incomplete and ambiguous training samples. It also discusses some experimental results from using this method for discovering knowledge on water demand prediction.