Identification of river water quality using the Fuzzy Synthetic Evaluation approach

Chang, Ni‐Bin; Chen, H.W.; Ning, Shu‐Kuang

doi:10.1006/jema.2001.0483

Cited by 277 publications

(129 citation statements)

References 17 publications

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“…The fuzzy composite operator is critical and affects the final evaluation results. The weighted average fuzzy composite operator is used widely in a variety of resource and environmental evaluation systems [43,44]. The weighted average fuzzy composite is shown in the following equation: b determines the level of hazard degree.…”

Section: Fuzzy Synthetic Evaluationmentioning

confidence: 99%

Hazard Assessment of Debris Flows in the Reservoir Region of Wudongde Hydropower Station in China

et al. 2015

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Abstract:The outbreak of debris flows in a reservoir region can affect the stability of hydropower stations and threaten the lives of the people living downstream of dams. Therefore, determining the hazard degree of debris flows in a reservoir region is of great importance. SPOT5 remote sensing images and digital elevation models are introduced to determine the characteristics of debris-flow catchments. The information is acquired through comprehensive manual investigation and satellite image interpretation. Ten factors that influence debris flow are extracted for the hazard assessment. The weight of these factors is determined using the analytic hierarchy process method. As a multi-criterion decision analysis method, fuzzy synthetic evaluation is applied for hazard assessment.

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Section: Fuzzy Synthetic Evaluationmentioning

confidence: 99%

Hazard Assessment of Debris Flows in the Reservoir Region of Wudongde Hydropower Station in China

et al. 2015

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“…Chang et al [59] used the fuzzy synthetic evaluation approach to identify river water quality. Chen and Mynett [60] employed data mining techniques and heuristic knowledge in modelling the fuzzy logic of eutrophication in Taihu Lake.…”

Section: Fuzzy Inference Systemsmentioning

confidence: 99%

A review on integration of artificial intelligence into water quality modelling

Chau

2006

Marine Pollution Bulletin

212

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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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“…Fuzzy set theory concepts can be useful in ecological impact classifications [64], as they can provide an alternative approach to dealing with those problems in which the objectives and constraints are not well defined or the information about them is not precise. Chang et al [65] used the fuzzy synthetic evaluation approach to identify the quality of river water. Chen and Mynett [66] employed data mining techniques and heuristic knowledge in modeling the fuzzy logic of eutrophication in Taihu Lake.…”

Section: Fuzzy Inference Systemsmentioning

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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Identification of river water quality using the Fuzzy Synthetic Evaluation approach

Cited by 277 publications

References 17 publications

Hazard Assessment of Debris Flows in the Reservoir Region of Wudongde Hydropower Station in China

Hazard Assessment of Debris Flows in the Reservoir Region of Wudongde Hydropower Station in China

A review on integration of artificial intelligence into water quality modelling

A review on the integration of artificial intelligence into coastal modeling

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