DISCOVERING RULES FOR WATER DEMAND PREDICTION - AN ENHANCED ROUGH-SETAPPROACH (REPRINTED FROM PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL-INTELLIGENCE)

Authors
AN AJ SHAN N CHAN C CERCONE N ZIARKO W
Citation
Aj. An et al., DISCOVERING RULES FOR WATER DEMAND PREDICTION - AN ENHANCED ROUGH-SETAPPROACH (REPRINTED FROM PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL-INTELLIGENCE), Engineering applications of artificial intelligence, 9(6), 1996, pp. 645-653
Citations number
8
Categorie Soggetti
Computer Application, Chemistry & Engineering","Computer Science Artificial Intelligence",Engineering,"Robotics & Automatic Control","Engineering, Eletrical & Electronic
Journal title
Engineering applications of artificial intelligence → ACNP
ISSN journal
09521976
Volume
9
Issue
6
Year of publication
1996
Pages
645 - 653
Database
ISI
SICI code
0952-1976(1996)9:6<645:DRFWDP>2.0.ZU;2-J
Abstract
Prediction of consumer demands is a pre-requisite for optimal control of water distribution systems because minimum-cost pumping schedules c an be computed if water demands are accurately estimated This paper pr esents an enhanced rough-sets method for generating prediction rules f rom a set of observed data. The proposed method extends upon the stand ard rough set model by making use of the statistical information inher ent in the data to handle incomplete and ambiguous training samples. I t also discusses some experimental results from using this method for discovering knowledge on water demand prediction. Copyright (C) 1996 I JCAI Inc.