The ‘WaterWare’ decision-support system for river-basin planning. 1. Conceptual design

Jamieson, D. G.; Fedra, K.

doi:10.1016/0022-1694(95)02957-5

Cited by 102 publications

(26 citation statements)

References 6 publications

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“…Chau and Yang [13] implemented an integrated expert system for fluvial hydrodynamics. Jamieson and Fedra [30] developed a decision-support system for efficient river basin planning and management. Ghosh Bobba et al [31] applied environmental models through an intelligent system to different hydrological systems.…”

Section: Knowledge-based Systems (Kbss)mentioning

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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Section: Knowledge-based Systems (Kbss)mentioning

confidence: 99%

A review on integration of artificial intelligence into water quality modelling

Chau

2006

Marine Pollution Bulletin

218

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“…Chau and Yang [31] implemented an integrated expert system for fluvial hydrodynamics. Jamieson and Fedra [32] developed a decision-support system for efficient river basin planning and management. Bobba et al [33] applied environmental models through an intelligent system to different hydrological systems.…”

Section: Knowledge-based Systems (Kbss)mentioning

confidence: 99%

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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“…(JAMIESON et FEDRA, 1996) sont probablement les SMI parmi les mieux intégrés. D'ailleurs, GIBSl a été conçu pour faciliter au maximum l'utilisation par les décideurs alors que WaterWare est surtout destiné aux spécialistes qui l'utiliseront à des fins de support à la gestion.…”

Section: Tableauunclassified

Utilisation des outils numériques d'aide à la décision pour la gestion de l'eau

Dupont¹,

Smitz²,

Rousseau³

et al. 2005

rseau

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Le succès d'une gestion des écosystèmes naturels requiert une connaissance approfondie des différents processus qui interviennent et de leurs échelles de temps et d'espace particulières. Pour cette raison, les décideurs ont besoin d'analyser une vaste gamme de données et d'informations géographiques. Les modèles mathématiques, les systèmes d'informations géographi-ques et les systèmes experts sont capables de produire cette analyse, mais seule une minorité de gestionnaires les utilise actuellement. Cet article identifie quelques unes des raisons à l'origine de l'hésitation des gestionnaires à adopter de tels outils d'aide à la décision pour la gestion des ressources naturelles et propose une structure qui pourrait faciliter leur utilisation pour le processus de prise de décision. Cet exercice est réalisé à l'intérieur du contexte de la gestion intégrée par bassin. Une revue des systèmes d'aide à la décision est également présentée.Many methods of integrated or watershed management exist which account for the necessary biophysical and socio-economic factors at the watershed level. Some of these approaches are ecosystem oriented while others are socio-economically oriented. Whatever the definition, water management at the watershed level needs to account for a plenitude of variables related to the air, water, soil, biology, and economy. The successful management of natural ecosystems requires a thorough understanding of their characteristic time and spatial scales. Because of this, decision makers need to analyze a wide range of data and geographic information. Mathematical models, geographic information systems and expert systems are capable of performing this analysis, but only a minority of managers are currently using them. This paper identifies some of the reasons why ecosystem managers have been slow to adopt such decision support tools in natural resources management and proposes a framework to facilitate their use in the decision making process. This is done in an integrated watershed management context. A review of related decision support systems is also presented.Four types of decision-support tools are introduced : mathematical models, expert-systems, geographical information systems (GIS) and decision support systems (DSS). Mathematical models have long been used for simulation, prediction, and forecasting, however, they are often task specific and were rarely developed for management uses. GIS are more and more commonly being used for decision support as they become more affordable and user-friendly and are very well-suited for managing resources at a spatial scale. There exist many kinds of software ranging from a simple viewer used for cartographic purposes to complex GIS oriented toward spatial analysis and modelling. Expert systems are also interesting for decision support when specif...

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The ‘WaterWare’ decision-support system for river-basin planning. 1. Conceptual design

Cited by 102 publications

References 6 publications

A review on integration of artificial intelligence into water quality modelling

A review on integration of artificial intelligence into water quality modelling

A review on the integration of artificial intelligence into coastal modeling

Utilisation des outils numériques d'aide à la décision pour la gestion de l'eau

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