Utility of fuzzy cross-impact simulation in environmental assessment

Parashar, Alka; Paliwal, Rakesh; Rambabu, P.

doi:10.1016/s0195-9255(97)00046-2

Cited by 29 publications

(8 citation statements)

References 7 publications

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“…(It is also possible to use a range of Ϫ1 to ϩ1 in cases where negative values are meaningful.) FCMs are closely related to other system and knowledge representation approaches (Marchant 1999) such as influence diagrams (Buede and Ferrell 1993), artificial neural net models of causality and fuzzy evidential logic (Sun 1994), crossimpact simulation (Parashar et al 1997) and fuzzy relational equations (Pedrycs 1991a, b). Each of these approaches involves definition of causal concepts and edges that designate direction, sign, and, in some cases, magnitude of influence on state variables of interest.…”

Section: Ecosystem Modeling Using Fuzzy Cognitive Mapsmentioning

confidence: 99%

Fuzzy Cognitive Mapping as a Tool to Define Management Objectives for Complex Ecosystems

Hobbs

Ludsin

Knight

et al. 2002

Ecological Applications

135

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Defining objectives for ecological rehabilitation requires consideration of how an ecosystem responds to management. Validated quantitative models of physical, chemical, and biological processes are the best way to project such impacts; however, time, data, and model limitations often make these approaches impractical. An alternative is to encode expert knowledge about interactions among ecosystem components in a fuzzy cognitive map (FCM), which then translates that subjective, qualitative information into predictions of the effects of management on an ecosystem. Herein, we present the steps involved in constructing an FCM of an ecosystem, interpreting FCM output using multivariate statistics, and portraying the information in an easily communicated fashion. To illustrate these ideas, we rely on a complex (>160 variables) ecosystem model built for the Lake Erie watershed under the auspices of the Lake Erie Lakewide Management Plan (LaMP). Based on our experiences in building this model, we also offer recommendations for increasing the efficiency of the model‐development and interpretation process. Use of the FCM method in this case promoted constructive interaction among dozens of scientists, managers, and the public, as well as providing insights concerning the potential effects of broad classes of management actions upon the Lake Erie ecosystem. The analysis focused the attention of participants on four broad alternatives for the Lake. One represents present conditions, and another results from a decrease in nutrient inputs but an increase in stresses from land use and human disturbance. The two others involve reduced stress from nutrients and land use, with one having relatively more nutrients and less human disturbance and fishing. The latter ecosystem alternative was tentatively endorsed by LaMP management, and all four alternatives will be reviewed by the public.

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Section: Ecosystem Modeling Using Fuzzy Cognitive Mapsmentioning

confidence: 99%

Fuzzy Cognitive Mapping as a Tool to Define Management Objectives for Complex Ecosystems

Hobbs

Ludsin

Knight

et al. 2002

Ecological Applications

135

View full text Add to dashboard Cite

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“…Asan et al [23] also proposed a fuzzy methodology for cross-impact analysis and used four different analysis methods in parallel to each research question followed by comparison of the results. Parashar et al [24] use a fuzzy cross-impact simulation in which the interaction in a studied system is represented by a CIM with linguistic terms. The methodology is used to visualize the dynamic development of the system in question.…”

Section: Simulation Controlmentioning

confidence: 99%

Simulation-based decision support for evaluating operational plans

Schubert

Moradi

Asadi

et al. 2015

Operations Research Perspectives

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“…For example, the random data are handled by the statistics and probabilities theories (Shannon and Weaver 1949); the fuzzy data are pursued by 2904 J. W. K. Chan the concept of fuzzy sets (Zedah 1975); the rough data are reasoned by the rough sets theory (Pawlak 1991); and the unascertained data are assimilated by the theory of unascertained information (Liu et al 1999). For uncertainty analysis of environmental impact assessment, various methodologies such as statistical analysis (Hanssen and Asbjornsen 1996), fuzzy logic (Parashar et al 1997), and the stochastic methods (Maurice et al 2000) are used. However, it is hardly possible to analyze all types of uncertainties.…”

Section: Eol Indicators and Measurementmentioning

confidence: 99%

Product end-of-life options selection: grey relational analysis approach

Chan

2008

International Journal of Production Research

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Management of the product end-of-life (EOL) for a manufacturing enterprise is important. An improper EOL strategy can negatively affect the productivity and profitability and undermines the reputation of an enterprise because of the growing demands for extended producer responsibility. Producers need tools and methods to evaluate each EOL option since it is a complicated but mustbe-accomplished task to achieve in order to solve the multiple-criteria problem that combines aspects such as eco-system quality, environmental impacts, human health issues, and economic factors etc. This paper presents an alternative decision-making process to generate an optimal solution from a list of EOL options under the uncertainty condition of incomplete information. Using Grey Relational Analysis, the multi-criteria weighted average is proposed to rank the product EOL options with respect to several criteria at the material level. It will guide the selection process and help a decision-maker solve the selection problem. The method is demonstrated with an example. Various EOL options are evaluated using the developed multicriteria methodology that takes account the environmental, economical and social factors.

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Utility of fuzzy cross-impact simulation in environmental assessment

Cited by 29 publications

References 7 publications

Fuzzy Cognitive Mapping as a Tool to Define Management Objectives for Complex Ecosystems

Fuzzy Cognitive Mapping as a Tool to Define Management Objectives for Complex Ecosystems

Simulation-based decision support for evaluating operational plans

Product end-of-life options selection: grey relational analysis approach

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