A study into the feasibility of an intelligent knowledge based system (IKBS) in computational fluid mechanics (CFM)

Applied Mathematical Modelling

Chen²

2001

Nowadays, artificial intelligence (AI) technology is gradually integrated into the numerical modelling system to make the system more intelligent and more user-friendly. The characteristics of the fifth generation numerical modelling are connected with AI applications. The expert system technology as a widely applied AI technology, is integrated into our modelling system for coastal water processes with traditional numerical computational tools and the data and graphical pre-processing and post-processing techniques. Five kinds of knowledge bases are built in the system to describe the existing expertise knowledge about model parameters, relations between parameters and physical conditions, various possible selections for parameters and rules of inference. The inference engine is designed to be driven by the confidence of correctness, and the rule base is built with the factor of confidence to link the various relations. The decision tree is designed to drive the inference engine to explore the route of selection procedure of modelling. The decision tree depends on the real problem specifications and can be modified during the dialogue between the system and the user. The forward chaining and backward chaining inference techniques are mixed together in the system to help matching the parameters in the model and the possible selections with sufficiently high confidence. The expert system technology is successfully integrated into the system to provide help for model parameter selection or model selection, and to make the numerical model system more accessible for non-expert users.

Section: Introductionmentioning

confidence: 99%

A fifth generation numerical modelling system in coastal zone

Applied Mathematical Modelling

Chen²

2001

“…Recently, some literatures about study into the feasibility of integrating KBS with the numerical modelling can be found [28][29]. Most of this kind of the fifth generation numerical modelling system in coastal area only refers to a one-dimensional modelling system for river network or river planning due to the simplicity of knowledge and selection procedure.…”

Section: Knowledge-based Systems (Kbss)mentioning

confidence: 99%

A review on integration of artificial intelligence into water quality modelling

Marine Pollution Bulletin

2006

218

With the development of computing technology, numerical models are often employed to simulate flow and water quality processes in coastal environments. However, the emphasis has conventionally been placed on algorithmic procedures to solve specific problems. These numerical models, being insufficiently user-friendly, lack knowledge transfers in model interpretation. This results in significant constraints on model uses and large gaps between model developers and practitioners. It is a difficult task for novice application users to select an appropriate numerical model. It is desirable to incorporate the existing heuristic knowledge about model manipulation and to furnish intelligent manipulation of calibration parameters. The advancement in artificial intelligence (AI) during the past decade rendered it possible to integrate the technologies into numerical modelling systems in order to bridge the gaps. The objective of this paper is to review the current state-of-the-art of the integration of AI into water quality modelling. Algorithms and methods studied include knowledge-based system, genetic algorithm, artificial neural network, and fuzzy inference system. These techniques can contribute to the integrated model in different aspects and may not be mutually exclusive to one another. Some future directions for further development and their potentials are explored and presented.

“…KBSs are considered suitable for solving problems that demand considerable expertise, judgment, or rules of thumb. KBSs have widespread applications in different fields and are able to accomplish a level of performance comparable to that of a human expert [27][28][29][30][31].…”

Section: Knowledge-based Systems (Kbss)mentioning

confidence: 99%

“…The feasibility of integrating KBS with numerical modeling has recently been studied [29][30]. Chau and Yang [31] implemented an integrated expert system for fluvial hydrodynamics.…”

Section: Knowledge-based Systems (Kbss)mentioning

confidence: 99%

A review on the integration of artificial intelligence into coastal modeling

Journal of Environmental Management

2006

With the development of computing technology, mechanistic models are often employed to simulate coastal processes in coastal environments. However, these predictive tools are inevitably highly specialized, involving certain assumptions and/or limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying theories. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and practitioners of a model. The recent advancements in artificial intelligence (AI) technologies are making it possible to integrate machine learning capabilities into numerical modeling systems in order to bridge the gaps and lessen the demands on human experts. The objective of this paper is to review the state-of-the-art in the integration of different AI technologies into coastal modeling. The algorithms and methods studied include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. More focus is given to knowledge-based systems, which has apparent advantages over the others in allowing more transparent transfers of knowledge in the use of models and in furnishing the intelligent manipulation of calibration parameters. Of course, the other AI methods also have their individual contributions towards accurate and reliable predictions of coastal processes. The resulting tool might be very powerful, since the advantages of each technique can be combined.