A fifth generation numerical modelling system in coastal zone

2006

Journal of Hydrology

298

115

An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks (ANN) has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks such as very slow convergence and easy entrapment in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is applied to predict water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown that the PSO technique can act as an alternative training algorithm for ANNs.

Section: The Study Areamentioning

confidence: 99%

Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River

2006

Journal of Hydrology

298

115

“…Ragas et al [11] suggested, though they did not actually implement, the development of an expert system for model selection in order to deal with uncertainty in model predictions, after compared a number of UK and USA models. Nowadays, the fifth generation modelling system [20,24,25] is acknowledged to have the features of integrating AI technology and computational hydrodynamics into a single system to furnish assistance for non-experienced user.…”

Section: Tendency In Modellingmentioning

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.

“…After comparing a number of U.K. and U.S.A. models, Ragas et al [11] suggested, although they did not actually implement, the development of a KBS for model selection in order to deal with uncertainty in model predictions. The fifth generation of modeling system [17,25,26] is acknowledged to have the features to allow AI technology and computational hydrodynamics to be integrated into a single system to furnish assistance for non-experienced users.…”

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.