Knowledge management system on flow and water quality modeling

被引:54
作者
Chau, KW [1 ]
Chuntian, C
Li, CW
机构
[1] Hong Kong Polytech Univ, Dept Civil & Struct Engn, Hong Kong, Hong Kong, Peoples R China
[2] Dalian Univ Technol, Dept Civil Engn, Dalian 116024, Peoples R China
基金
中国国家自然科学基金;
关键词
knowledge management system; flow and water quality modeling; artificial intelligence; object-oriented programming; production rule; visual interface;
D O I
10.1016/S0957-4174(02)00020-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2002 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:321 / 330
页数:10
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