A Fuzzy Logic Approach to Assess, Manage, and Communicate Carcinogenic Risk

Matbouli, Yasser T.; Hipel, Keith W.; Kilgour, D. Marc; Karray, Fakhri

doi:10.1080/10807039.2013.862111

Cited by 6 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy set theory has been applied to quantify uncertainty in risk assessment (Arunraj et al., 2013; Kentel & Aral, 2005), and it allows the utilization of incomplete information when there is insufficient data to generate probability distributions for the parameters of the random variables of the risk equation (Kentel & Aral, 2005). Moreover, fuzzy‐based approaches are often the method of choice for handling uncertainties, including analyzing soft factors that are difficult to quantify (Loh et al., 2020; Matbouli et al., 2014). Consequently, fuzzy logic and expert judgments are widely used in several operational risk assessment studies such as in autonomous underwater vehicles (Brito & Dawson, 2020; Loh et al., 2020), in inland waterways (Martins et al., 2020), in nuclear power plants (Rastogi & Gabbar, 2013), and oil and gas industry (Mun, 2004).…”

Section: Methodsmentioning

confidence: 99%

An integrated environmental risk assessment framework for coal‐fired power plants: A fuzzy logic approach

Bartan

Küçükali

et al. 2022

Risk Analysis

View full text Add to dashboard Cite

This study quantifies the environmental risk of a coal‐fired thermal power plant during operation by using environmental monitoring data, site surveys, and documented evidence. The following criteria are assessed: emissions (CO, SO2, NOx, PM10), impact on aquatic ecosystem (fish protection at cooling water intake and cooling water discharge temperature), and waste management (fly ash and bottom ash). Fuzzy sets were defined for each criterion, taking environmental regulatory context as an expert judgment. A survey was conducted with multiple stakeholders to determine the relative importance of risk factors. The survey results showed that the most concerned risks are SO2 and NOx emissions. The proposed method estimates the risk of each environmental criterion separately and then accumulates them into an environmental risk index (ERI). Accordingly, we assessed the Catalagzi coal‐fired power plant, which has been in operation on the Black Sea coast in northwestern Turkey. For this case study, the ERI resulted in a value of 0.78 (on a scale of 0–1), showing high environmental risk to the facility. Moreover, the applicability of the proposed framework was tested in several existing coal‐fired power plants using simultaneous measurements. All studied coal‐fired power plants in Turkey have unacceptable pollutants (PM10, SO2, and NOx) concentration levels indicating high health risk potential. The application of the integrated environmental risk assessment framework showed that new environmental regulations are needed in Turkey to specify more strict emission limits and to monitor CO2, fine particulate matter emissions, cooling water discharge, and fish protection at cooling water intake.

show abstract

Section: Methodsmentioning

confidence: 99%

An integrated environmental risk assessment framework for coal‐fired power plants: A fuzzy logic approach

Bartan

Küçükali

et al. 2022

Risk Analysis

View full text Add to dashboard Cite

show abstract

“…Climate is a consistent phenomenon, information concentrated, dynamic and random process [1]. The parameters required to anticipate climate are colossally intricate with the end goal that there is a vulnerability in forecast notwithstanding for a brief period [2].…”

Section: Introductionmentioning

confidence: 99%

Weather Forecasting Pridiction using Mamdani Fuzzifier

Zahra

Abbas

2019

Research Journal Of Computer Science And Information Technology

View full text Add to dashboard Cite

A climate expectation display is under study in view of the neural system and fuzzy surmising framework, and after that apply it to anticipate every day fuzzy precipitation given meteorological premises for testing. A "fuzzy ranked based neural system", which reenacts successive relations among fuzzy sets utilizing the manufactured neural system. It is outstanding that the requirement for exact climate expectation is clear while thinking about the advantages. Nonetheless, the over the top quest for exactness in climate expectation makes a portion of the "precise" forecast comes about pointless and the numerical forecast show is regularly intricate and tedious.

show abstract

“…Li et al (2008) applied a similar approach to assessing the health-impact risk from air pollution. Matbouli et al (2014) reported the use of fuzzy logic in the context of prospective assessment of cancer risks.…”

Section: Expressing Uncertainty Using Possibilitymentioning

confidence: 99%

The principles and methods behind EFSA's Guidance on Uncertainty Analysis in Scientific Assessment

Benford¹,

Halldórsson²,

Jeger³

et al. 2018

EFS2

139

134

View full text Add to dashboard Cite

To meet the general requirement for transparency in EFSA's work, all its scientific assessments must consider uncertainty. Assessments must say clearly and unambiguously what sources of uncertainty have been identified and what is their impact on the assessment conclusion. This applies to all EFSA's areas, all types of scientific assessment and all types of uncertainty affecting assessment. This current Opinion describes the principles and methods supporting a concise Guidance Document on Uncertainty in EFSA's Scientific Assessment, published separately. These documents do not prescribe specific methods for uncertainty analysis but rather provide a flexible framework within which different methods may be selected, according to the needs of each assessment. Assessors should systematically identify sources of uncertainty, checking each part of their assessment to minimise the risk of overlooking important uncertainties. Uncertainty may be expressed qualitatively or quantitatively. It is neither necessary nor possible to quantify separately every source of uncertainty affecting an assessment. However, assessors should express in quantitative terms the combined effect of as many as possible of identified sources of uncertainty. The guidance describes practical approaches. Uncertainty analysis should be conducted in a flexible, iterative manner, starting at a level appropriate to the assessment and refining the analysis as far as is needed or possible within the time available. The methods and results of the uncertainty analysis should be reported fully and transparently. Every EFSA Panel and Unit applied the draft Guidance to at least one assessment in their work area during a trial period of one year. Experience gained in this period resulted in improved guidance. The Scientific Committee considers that uncertainty analysis will be unconditional for EFSA Panels and staff and must be embedded into scientific assessment in all areas of EFSA's work.

show abstract

A Fuzzy Logic Approach to Assess, Manage, and Communicate Carcinogenic Risk

Cited by 6 publications

References 19 publications

An integrated environmental risk assessment framework for coal‐fired power plants: A fuzzy logic approach

An integrated environmental risk assessment framework for coal‐fired power plants: A fuzzy logic approach

Weather Forecasting Pridiction using Mamdani Fuzzifier

The principles and methods behind EFSA's Guidance on Uncertainty Analysis in Scientific Assessment

Contact Info

Product

Resources

About