Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
INFER: an adaptative decision support system based on the probabilistic approximate classification
6th Internation Workshop Vol. 1 on Expert Systems & Their Applications
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Time series modelling of water demand—a study on short-term and long-term predictions
Computer applications in water supply: vol. 1---systems analysis and simulation
Variable precision rough set model
Journal of Computer and System Sciences
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Machine Learning
Variable Precision Rough Sets with Asymmetric Bounds
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Consistency and Completeness in Rough Sets
Journal of Intelligent Information Systems
The Variable Precision Rough Set Model for Web Usage Mining
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Neural and rough set based data mining methods in engineering
Handbook of data mining and knowledge discovery
Rough Set-Based Clustering with Refinement Using Shannon's Entropy Theory
Computers & Mathematics with Applications
Multi-agent control system for a municipal water system
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Review: Dimensionality reduction based on rough set theory: A review
Applied Soft Computing
An Incremental Approach for Inducing Knowledge from Dynamic Information Systems
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Classification systems based on rough sets under the belief function framework
International Journal of Approximate Reasoning
An Incremental Approach for Inducing Knowledge from Dynamic Information Systems
Fundamenta Informaticae - Fundamentals of Knowledge Technology
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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.