Application of two-stage fuzzy set theory to river quality evaluation in Taiwan

Liou, S.M.; Lo, Shang Lien; Hu, Ching Yao

doi:10.1016/s0043-1354(02)00479-7

Cited by 97 publications

(56 citation statements)

References 29 publications

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“…Today, access to clean water is becoming a difficult task in many regions of the world. According to the World Health Organization, 1.2 billion people lack access to sufficient amounts of clean fresh water and 2.6 billion lack adequate sanitation [3]. Poor sanitation combined with unhealthy water quality accounts for the largest single cause for disease and death in the world.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO₂) by Using Surface Coating Method

et al. 2018

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Abstract. In this study, cellulose acetate/polyethylene glycol (CA/PEG) membrane with composition 80/20 was prepared by phase inversion method. Titanium dioxide with different number has been added by using surface coating. Hydrophilicity, morphology, flux permeate and salt rejection of membranes has been studied. The hydrophilicity is determined by FourierTransformed Infra-Red (FTIR) spectra and contact angle analysis. Surface and fractured morphology are identified by using Scanning Electron Microscopy (SEM). The experiment results show that hydrophilicity of CA/PEG membrane increases with the addition and the increasing of TiO2 contents. However, with further increasing of TiO2, hydrophilicity of CPT membrane decreases. The optimum membrane is CA/PEG/TiO2 80/20/1,25 g/L solvent (CPT 3) with flux permeate of 111,82 L.m-2h-1 and salt rejection of 48,30%.

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Section: Introductionmentioning

confidence: 99%

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO₂) by Using Surface Coating Method

et al. 2018

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“…Chen and Mynett [60] employed data mining techniques and heuristic knowledge in modelling the fuzzy logic of eutrophication in Taihu Lake. Liou et al [61] applied two-stage fuzzy set theory to river quality evaluation in Taiwan. Marsili-Libelli [62] described the design of a bloom predictor based on the daily fluctuations of simple water quality parameters such as dissolved oxygen, oxidation-reduction potential, pH and temperature.…”

Section: Fuzzy Inference Systemsmentioning

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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“…Chen and Mynett [66] employed data mining techniques and heuristic knowledge in modeling the fuzzy logic of eutrophication in Taihu Lake. Liou et al [67] applied a two-stage fuzzy set theory to evaluate river quality in Taiwan. Marsili-Libelli [68] described the design of a bloom predictor based on the daily fluctuations of simple parameters for water quality such as dissolved oxygen, oxidation-reduction potential, pH, and temperature.…”

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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Application of two-stage fuzzy set theory to river quality evaluation in Taiwan

Cited by 97 publications

References 29 publications

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO₂) by Using Surface Coating Method

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO₂) by Using Surface Coating Method

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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Application of two-stage fuzzy set theory to river quality evaluation in Taiwan

Cited by 97 publications

References 29 publications

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO2) by Using Surface Coating Method

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO2) by Using Surface Coating Method

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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Product

Resources

About

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO₂) by Using Surface Coating Method

Enhancing Properties and Performance of Cellulose Acetate/Polyethylene Glycol (CA/PEG) Membrane with the addition of Titanium Dioxide (TiO₂) by Using Surface Coating Method