Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1234
Title: Knowledge management system on flow and water quality modeling
Authors: Chau, Kwok-wing
Cheng, Chuntian
Li, C. W.
Subjects: Knowledge management system
Flow and water quality modeling
Artificial intelligence
Object-oriented programming
Production rule
Visual interface
Issue Date: May-2002
Publisher: Pergamon (Elsevier)
Source: Expert systems with applications, May 2002, v. 22, no. 4, p. 321-330.
Abstract: Due to the complexity of the numerical simulation of flow and/or water quality, there is an increasing demand for integration of recent knowledge management, artificial intelligence technology with the conventional hydraulic algorithmic models in order to assist novice application users in selection and manipulation of various mathematical tools. In this paper, a prototype knowledge management system on flow and water quality is addressed to simulate human expertise during the problem solving by incorporating artificial intelligence and coupling various descriptive knowledge, procedural knowledge and reasoning knowledge involved in the coastal hydraulic and transport processes. The system is developed through employing Visual Rule Studio, a hybrid expert system shell, as an ActiveX Designer under Microsoft Visual Basic 6.0 environment since it combines the advantages of both production rules and object-oriented programming technology. The architecture, the development and the implementation of the prototype system are delineated in details. Based on the succinct features and conditions of a variety of flow and water quality models, three kinds of class definitions, Section and Problem as well as Question are defined and the corresponding knowledge rule sets are also established. Both forward chaining and backward chaining are used collectively during the inference process. A typical example is also presented to demonstrate the application of the prototype knowledge management system.
Rights: Expert Systems with Applications © 2002 Elsevier Science Ltd. The journal web site is located at http://www.sciencedirect.com.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1234
DOI: 10.1016/S0957-4174(02)00020-9
ISSN: 0957-4174
Appears in Collections:CEE Journal/Magazine Articles

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