A finite difference model of two-dimensional tidal flow in Tolo Harbor, Hong Kong

Chau, Kwok Wing; Jin, Haisheng; Sin, Y. S.

doi:10.1016/0307-904x(95)00127-6

Cited by 26 publications

(21 citation statements)

References 6 publications

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“…It has incorporated very few features to facilitate other users and to handle other problems. Typical examples are some sophisticated convectiondispersion models of the Eulerian-Lagrangian type [2], two-dimensional or three-dimensional finite difference numerical models on tidal flow [4,5] and on a specific water quality phenomenon such as eutrophication [21], finite-element modeling of floodplain flow [6], the depth-averaged turbulence k-e model [9], and so on.…”

Section: Generations Of Modelingmentioning

confidence: 99%

“…The shape function can be of the first order, second order, or a higher order. The modeling can be simplified into different spatial dimensions, i.e., a one-dimensional model, two-dimensional depth-averaged model, two-dimensional layered model, three-dimensional model, and so forth [1][2][3][4][5][6][7][8]. An analysis of coastal hydraulics and water quality generally involves heuristics and empirical experience, and is effected through some simplifications and modeling techniques on the basis of the experience of specialists [9].…”

Section: Introductionmentioning

confidence: 99%

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A review on the integration of artificial intelligence into coastal modeling

Chau

2006

Journal of Environmental Management

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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.

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Section: Generations Of Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on the integration of artificial intelligence into coastal modeling

Chau

2006

Journal of Environmental Management

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“…The shape function can be of first order, second order or higher order. The modelling can be simplified into different spatial dimensions, i.e., 1-dimensional model, 2-dimensional depth-averaged model, 2-dimensional layered model, 3-dimensional model, etc [1][2][3][4][5][6][7][8]. The analysis of coastal hydraulics and water quality generally involves heuristics and empirical experience and it is effected through some simplifications and modelling techniques on the basis of the experience of specialists [9].…”

Section: Introductionmentioning

confidence: 99%

A review on integration of artificial intelligence into water quality modelling

Chau

2006

Marine Pollution Bulletin

218

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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.

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“…Coupled with the rapid growth in economic development, the increasing tendency of eutrophication phenomena in Tolo Harbour is evidenced by a drastic increase in the severity and frequency of algae blooms (EPDHK, 2003). Various problems, including undesirable water quality, bottom-water anoxia, decline in fisheries, and loss of submerged aquatic vegetation, etc., have resulted (Hodgkiss and Chan, 1983;Sin and Chau, 1992;Chau et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

“…and recording the effects of development on levels of pollution in the water Chau et al, 1996;Hodgkiss and Chan, 1983;Sin and Chau, 1992). With the recent rapid advancement of computer technology, the domain problem can now be epitomized fully in an unsteady manner.…”

Section: Introductionmentioning

confidence: 99%

An unsteady three-dimensional eutrophication model in Tolo harbour, Hong Kong

Chau¹

2005

Marine Pollution Bulletin

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The eutrophication phenomenon often leads to undesirable water quality. This paper delineates an unsteady three-dimensional finite difference numerical model for eutrophication dynamics in the coastal waters of Tolo Harbour, Hong Kong, employing the numerically generated, boundary-fitted, orthogonal curvilinear grid system as well as a grid "block" technique. It models the transport and interaction of nine water quality constituents. Adjustments of values of some kinetic coefficients in the model are effected through calibration with field data. It is demonstrated that the model can reasonably reproduce the interactions amongst all the water quality constituents, the eutrophication processes and, in particular, the featured bottom water anoxic condition during the summer in Tolo Harbour.

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A finite difference model of two-dimensional tidal flow in Tolo Harbor, Hong Kong

Cited by 26 publications

References 6 publications

A review on the integration of artificial intelligence into coastal modeling

A review on the integration of artificial intelligence into coastal modeling

A review on integration of artificial intelligence into water quality modelling

An unsteady three-dimensional eutrophication model in Tolo harbour, Hong Kong

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