Can Fuzzy Logic Bring Complex Environmental Problems into Focus?

McKone, Thomas E.; Deshpande, Ashok

doi:10.1021/es0531632

Cited by 74 publications

(31 citation statements)

References 8 publications

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“…During the last decade, different applications of fuzzy logic in environmental risk assessment have been discussed in papers. Ma (2002), Dahab et al (1994), Uricchio et al (2004), McKone and Deshpande (2005), and Darbra et al (2008b) established different FIS for evaluating risk in environmental issues. Finally, Kentel and Aral (2007) and Vemula et al (2004) tried to use a hybrid of probabilistic and fuzzy methods to analyze environmental risks.…”

Section: Dnvmentioning

confidence: 99%

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Karimpour

Zarghami

Moosavian

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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-A new risk assessment methodology by using fuzzy logic is proposed in this paper. The new Fuzzy Inference System (FIS) was established by the Mamdani algorithm based on different consequences of an incident. A combination of two FIS formed the proposed fuzzy method. Human knowledge and brainstorming were the devices for making the rules and interdependencies between variables in the new model. Different types of consequences and effective parameters were considered as inputs for the first fuzzy inference system. The final consequence was the preliminary result of the first inference model. It added to the probability of failures, as inputs of the second inference model. The result of the second inference model was the risk factor, which was considered as the final output of the proposed new fuzzy model. This model makes risk assessment more convenient in the absence of suitable data. In addition, decision-making will be easier, since its results are more understandable than the results of classical methods. A case study and a comparison between the classic method and the new fuzzy model illustrated that the results of the proposed model are more accurate, reliable and convenient for use in decision-making.Résumé -Nouveau modèle à logique floue pour une évaluation de risque basée sur différents types de conséquences -Une nouvelle méthode d'évaluation des risques en utilisant une logique floue est proposée dans le présent article. Le nouveau système d'inférence flou a été établi en utilisant l'algorithme de Mamdani basé sur les différentes conséquences d'un incident. Une combinaison de deux systèmes d'inférence flous a donné naissance à la méthode floue proposée. Les connaissances humaines et un brainstorming ont été les outils qui ont permis d'établir les règles et interdépendances entre des variables du nouveau modèle. Différents types de conséquences et de paramètres effectifs ont été pris en considération comme entrées pour le premier système d'inférence flou. La conséquence finale qui est le résultat préliminaire du premier modèle d'inférence, est combinée avec la probabilité de risques, en tant qu'entrées du second modèle d'inférence. Cette combinaison conduit à considérer le facteur de risque comme le résultat final du nouveau modèle flou proposé. Ce modèle rend l'évaluation des risques plus facile, en l'absence de données adaptées. En outre, la prise de décision sera plus simple, puisque ses résultats sont plus compréhensibles que les résultats des méthodes classiques. Une étude de cas et une comparaison entre la méthode classique et le nouveau modèle flou ont démontré que les résultats du modèle proposé étaient plus précis, plus fiables et plus faciles à utiliser dans la prise de décision.

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Section: Dnvmentioning

confidence: 99%

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Karimpour

Zarghami

Moosavian

et al. 2014

Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles

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“…The final aim is to reduce the uncertainty and imprecision in criteria employed in decision-making tools. [6,10,23] It is proposed by many researchers to use the methods based on fuzzy sets theory to handle the uncertainty involved in analysis of Water, Air, Land, Biological and socio-economic concepts. Keeping the importance of uncertainty handling in the environmental quality assessment and versatility of the fuzzy set theory in the decision making in the imprecise environment', continuous attempts are being made to describe the environmental quality by considering the fuzzy set theory.…”

Section: Introductionmentioning

confidence: 99%

Use of Fuzzy Set Theory in Environmental Engineering Applications: A Review

Khan¹

2017

IJERA

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Methods of solving the identified environmental problems, considering mathematical rigorous alternative assessment of environmental component process using fuzzy logic and approximate reasoning, are described by various researchers. To illustrate how such a computational intelligence approach would work in performing an assessment, various artificial techniques have been described. Fuzzy system technique for analysis of environmental components differentiates the approach from those techniques used in the past. It takes advantage of advanced computational intelligence techniques such as fuzzy sets and logic, for quantifying and manipulating in a mathematically rigorous way, subjective, inherently uncertain or imprecise values and concepts. This paper put forth the use of fuzzy sets in field of environmental engineering.

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“…Internationally, there have been a number of attempts to produce a method that meaningfully integrates the data sets and converts them into information [7]. In modeling complex environmental problems, researchers often fail to make precise statements about inputs and outcomes, but fuzzy logic could be applied to the development of environment indices in a manner that solves several common problems, including the incompatibility of observations and the need for implicit value judgements [8]. Conventional water quality regulations contain quality classes which use crisp sets (crisp sets means classical sets express two level in classical mathematic, instead of multiple level among two level [0,1]) and the limits between different classes have inherent imprecision [9].…”

Section: Introductionmentioning

confidence: 99%

Development of New Water Quality Model Using Fuzzy Logic System for Malaysia

Abdullah¹,

Waseem²,

Bai³

2008

TOENVIRSJ

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Proper assessment of water quality status in a river system based on limited observations is an essential task for meeting the goals of environmental management. Various classification methods have been used for estimating the changing status and usability of surface water in River basin. However, a discrepancy frequently arises from the lack of a clear distinction between each water utilization mode, the uncertainty in the quality criteria of water employed and vagueness or fuzziness embedded in the decision making output values. Owing to inherent imprecision, difficulties always exist in some conventional methologies like water quality index (WQI) when describing integrated water quality conditions with respect to various chemical constituents, biological aspects, nutrients, and aesthetic qualities. In recent years, the fuzzy logic based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental issues. In the present study, a methodology based on fuzzy inference systems (FIS) to assess water quality is proposed. This paper presents a comparative study using fuzzy logic technique to assess status of river water quality by comparing the output generated by fuzzy with that of conventional methods. The model is based on observations made from Semenyih River in West Malaysia. The findings clearly indicate that the fuzzy inference system (FIS) may successfully harmonize inherent discrepancies and interpret complex conditions. This river water quality model can be extended to determine the non-regulated contaminants in water.

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Can Fuzzy Logic Bring Complex Environmental Problems into Focus?

Cited by 74 publications

References 8 publications

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

New Fuzzy Model for Risk Assessment Based on Different Types of Consequences

Use of Fuzzy Set Theory in Environmental Engineering Applications: A Review

Development of New Water Quality Model Using Fuzzy Logic System for Malaysia

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