Fuzzy Cognitive Maps: Basic Theories and Their Application to Complex Systems

Groumpos, Peter P.

doi:10.1007/978-3-642-03220-2_1

Cited by 160 publications

(115 citation statements)

References 34 publications

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“…Fuzzy causal relations among concepts are generated by consulting the experts or general literature reviews. The fuzziness of these sources stems from the uncertain, complex and dynamic environments and decision factors in it [9,10]. FCM can be shown in a symbolical form with concepts or factors (nodes) and connections or causal relationships (arcs/edges).…”

Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

Modeling renewable energy usage with hesitant Fuzzy cognitive map

Çoban

Onar

2017

Complex Intell. Syst.

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Renewable energy sources (solar, wind, tidal, etc.), which are unlimited and have a fair distribution in the world, are an alternative to the depleting fossil fuels (coil, petroleum, natural gas, etc.). It is necessary to identify the right technologies and methods to make more effective use of renewable energy sources including uncertainty and irregularity in resource creation. In this study, dynamic environmental factors affecting the production of solar and wind energy are defined and the relations among them are linguistically expressed by the experts. These linguistic relationships among factors and their initial states are assessed by new developed hesitant linguistic cognitive map method that is an extension of hesitant fuzzy sets and fuzzy cognitive map. Relational development between factors was observed by simulating the model according to the initial condition of the factors. Thus, the model helps investors and governments to direct their solar and wind energy investment decisions.

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Section: Fuzzy Cognitive Mapmentioning

confidence: 99%

Modeling renewable energy usage with hesitant Fuzzy cognitive map

Çoban

Onar

2017

Complex Intell. Syst.

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“…These synthetic depthdamage functions were developed through a series of expert meetings and a workshop, following the fuzzy cognitive mapping method (Groumpos, 2010;Stach et al, 2010). The process consisted of two rounds.…”

Section: Vulnerabilitymentioning

confidence: 99%

River flood risk in Jakarta under scenarios of future change

Budiyono

Aerts

Tollenaar

et al. 2016

Nat. Hazards Earth Syst. Sci.

106

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Abstract. Given the increasing impacts of flooding in Jakarta, methods for assessing current and future flood risk are required. In this paper, we use the DamagescannerJakarta risk model to project changes in future river flood risk under scenarios of climate change, land subsidence, and land use change. Damagescanner-Jakarta is a simple flood risk model that estimates flood risk in terms of annual expected damage, based on input maps of flood hazard, exposure, and vulnerability. We estimate baseline flood risk at USD 186 million p.a. Combining all future scenarios, we simulate a median increase in risk of +180 % by 2030. The single driver with the largest contribution to that increase is land subsidence (+126 %). We simulated the impacts of climate change by combining two scenarios of sea level rise with simulations of changes in 1-day extreme precipitation totals from five global climate models (GCMs) forced by the four Representative Concentration Pathways (RCPs). The results are highly uncertain; the median change in risk due to climate change alone by 2030 is a decrease by −46 %, but we simulate an increase in risk under 12 of the 40 GCM-RCPsea level rise combinations. Hence, we developed probabilistic risk scenarios to account for this uncertainty. If land use change by 2030 takes places according to the official Jakarta Spatial Plan 2030, risk could be reduced by 12 %. However, if land use change in the future continues at the same rate as the last 30 years, large increases in flood risk will take place. Finally, we discuss the relevance of the results for flood risk management in Jakarta.

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“…Relative weights are used to quantify strengths of causal relationships between the factors (Kosko 1986). The weights of the interconnections can vary in the interval [-1,1], where the maximum and minimum values indicate a complete causal interrelationship among factors (Groumpos 2010). All weights attributed to the relationships can be represented as a matrix; initial values of the factors can be represented by a vector indicating the relative change of all factors, which is initially set at zero (see Kok 2009 for a detailed explanation).…”

Section: Principles Of Fuzzy Cognitive Mappingmentioning

confidence: 99%

Mapping future changes in livelihood security and environmental sustainability based on perceptions of small farmers in the Brazilian Amazon

Diniz¹,

Kok²,

Hoogstra-Klein³

et al. 2015

E&S

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Arts. 2015. Mapping future changes in livelihood security and environmental sustainability based on perceptions of small farmers in the Brazilian Amazon. ABSTRACT. Deforestation is a widely recognized problem in the Brazilian Amazon. Small farmers play a key role in this process in that they earn their livelihood by ranching and farming. Many studies have addressed the link between deforestation and livelihood strategies adopted by small farmers. Most have focused on advanced monitoring systems, simulation models, and GIS approaches to analyze the interaction of both dimensions, i.e., livelihoods and forest cover change. Although the current toolbox of methods has proved successful in increasing our understanding of these interactions, the models and approaches employed do not consider small farmers' perspectives. On the assumption that local small farmers are agents of land-cover change, understanding how they perceive their own situation is essential to elucidate their actions. Our objective is to explore future changes in livelihood security and environmental sustainability as envisaged by local small farmers in the Brazilian Amazon. Previous livelihood cluster analysis of small farmers located in southeast Pará was integrated with fuzzy cognitive mapping to determine present perceptions and to explore future changes, using global scenarios downscaled to the local situation. Overall, system description differs only on details; all results indicate a strong trade-off between livelihood security and environmental sustainability in all livelihood systems, as identified by the small farmers. However, fundamentally different outcomes are obtained from the future analysis, depending on the livelihood strategy cluster. Achieving win-win outcomes does not necessarily imply a positive scenario, especially if small farmers are dependent on income transfers from the government to provide their livelihood.

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Fuzzy Cognitive Maps: Basic Theories and Their Application to Complex Systems

Cited by 160 publications

References 34 publications

Modeling renewable energy usage with hesitant Fuzzy cognitive map

Modeling renewable energy usage with hesitant Fuzzy cognitive map

River flood risk in Jakarta under scenarios of future change

Mapping future changes in livelihood security and environmental sustainability based on perceptions of small farmers in the Brazilian Amazon

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