A cognitive map framework to support integrated environmental assessment

Mourhir, Asmaa; Rachidi, Tajjeeddine; Papageorgiou, Elpiniki I.; Karim, Mohamed Rehan; Alaoui, Faycal Sossi

doi:10.1016/j.envsoft.2015.11.018

Cited by 43 publications

(30 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FCM community has touched upon issues related to traceability in FCM building (e.g. Vanwindekens et al ., ) or started to collect common difficulties and pitfalls in FCM practices (Jetter and Kok, ), and there have been proposals to solve technical difficulties for individual map aggregation and condensation (Mourhir et al ., ; Obiedat and Samarasinghe, ). However, these authors also detect space for improvement at least in the reporting of source data, collection and treatment methods.…”

Section: Introductionmentioning

confidence: 99%

Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping

2018

View full text Add to dashboard Cite

By aggregating semi‐quantitative mind maps from multiple agents, fuzzy cognitive mapping (FCM) allows developing an integrated, cross‐sectoral understanding of complex systems. However, and especially for FCM based on individual interviews, the map‐building process presents potential pitfalls. These are mainly related to the different understandings of the interviewees about the FCM semantics as well as the biases of the analyst during the elicitation and treatment of data. This paper introduces a set of good practice measures to increase transparency and reproducibility of map‐building processes in order to improve credibility of results from FCM applications. The case study used to illustrate the proposed good practices assesses heatwave impacts and adaptation options in an urban environment. Agents from different urban sectors were interviewed to obtain individual cognitive maps. Using this set of data, we suggest good practices to collect, digitalize, interpret, pre‐process and aggregate the individual maps in a traceable and coherent way. © 2018 The Authors Systems Research and Behavioral Science published by International Federation for Systems Research and John Wiley & Sons Ltd

show abstract

Section: Introductionmentioning

confidence: 99%

Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping

2018

View full text Add to dashboard Cite

show abstract

“…DRBFCM was proposed as an alternative to SD using FCMs and FISs [40]. Concepts represent causes or effects that collectively characterize a system state at a given time.…”

Section: Dynamic Rule-based Fuzzy Cognitive Maps (Drbfcms)mentioning

confidence: 99%

“…In DRBFCM, the rules have a single antecedent related to a concept's state or variation, and a single consequent which is always a variation, representing a perturbation in the output concept. Since FCL supports only rules that map input concept states to output concepts states, the authors modified the FCL grammar to cope with rules describing concept variations [40]. The new FCL grammar has: (i) the "INˆID" clause added to the subcondition, which is used to specify the causal variation, where "IN" is a keyword and "ID" denotes the cause variable; and (ii) the "ONˆID" clause added to the subconclusion to specify the effect variation, where "ON" is a keyword and "ID" denotes the effect variable:…”

Section: Dynamic Rule-based Fuzzy Cognitive Maps (Drbfcms)mentioning

confidence: 99%

“…Concepts are considered to have memory with a self-feedback link weight equal to 1, so the activation value of the concept is updated by recalling its old value and adding it to the summation of weighted input activations. Since DRBFCM models operate on real deterministic values, the influencing concept's activation value is scaled to the universe of discourse of the output concept using Taylor-Young linear transformation [40].…”

Section: Dynamic Rule-based Fuzzy Cognitive Maps (Drbfcms)mentioning

confidence: 99%

“…Mourhir et al [40] proposed a Dynamic Rule-based Fuzzy Cognitive Map (DRBFCM) as a FCM extension, where the influence weights are depicted using nonlinear causal relationships in the form of FISs that are capable of adapting influence weights dynamically during every step of a simulation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:One of the major problems confronted in precision agriculture is uncertainty about how exactly would yield in a certain area respond to decreased application of certain nutrients. One way to deal with this type of uncertainty is the use of scenarios as a method to explore future projections from current objectives and constraints. In the absence of data, soft computing techniques can be used as effective semi-quantitative methods to produce scenario simulations, based on a consistent set of conditions. In this work, we propose a dynamic rule-based Fuzzy Cognitive Map variant to perform simulations, where the novelty resides in an enhanced forward inference algorithm with reasoning that is characterized by magnitudes of change and effects. The proposed method leverages expert knowledge to provide an estimation of crop yield, and hence it can enable farmers to gain insights about how yield varies across a field, so they can determine how to adapt fertilizer application accordingly. It allows also producing simulations that can be used by managers to identify effects of increasing or decreasing fertilizers on yield, and hence it can facilitate the adoption of precision agriculture regulations by farmers. We present an illustrative example to predict cotton yield change, as a response to stimulated management options using proactive scenarios, based on decreasing Phosphorus, Potassium and Nitrogen. The results of the case study revealed that decreasing the three nutrients by half does not decrease yield by more than 10%.

show abstract

Water pollution from pharmaceutical use in livestock farming: Assessing differences between livestock types and production systems

Wöhler

Hogeboom

Berger

et al. 2023

Integr Envir Assess & Manag

View full text Add to dashboard Cite

Livestock production is a major source of pharmaceutical emissions to the environment. The current scientific discourse focuses on measuring and modeling emissions as well as assessing their risks. Although several studies corroborate the severity of pharmaceutical pollution resulting from livestock farming, differences in pollution between livestock types and production systems are largely unknown. In fact, there is no comprehensive analysis of factors influencing pharmaceutical use—the emission's source—in the diverse production systems. To address these knowledge gaps, we developed a framework to investigate pharmaceutical pollution from different livestock production systems and applied it in a first pilot assessment to compare pollution from organic and conventional cattle, pig, and chicken production systems on selected indicator substances, covering antibiotics, antiparasitics, hormones, and nonsteroidal anti‐inflammatory drugs (NSAIDs). Given the lack of statistics, for this article we retrieved novel qualitative information about influential factors for pharmaceutical use and pollution from expert interviews and combined this with quantitative data on, among other factors, the environmental behavior of specific substances from the literature. Our analysis reveals that factors across a pharmaceutical's entire life cycle influence pollution. However, not all factors are livestock type or production‐system dependent. The pilot assessment furthermore reveals that differences in pollution potential between conventional and organic production exist, but for antibiotics, NSAIDs, and partially for antiparasitics, some factors lead to greater pollution potential in conventional systems, and others in organic systems. For hormones, we identified a comparatively greater pollution potential from conventional systems. Among the indicator substances, the assessment over the entire pharmaceutical life cycle illustrates that flubendazole in broiler production has the greatest per unit impact. The framework and its application in the pilot assessment generated insights useful to identifying which substances, livestock types, production systems, or the combination thereof have great or little pollution potential, informing more sustainable agricultural management practices. Integr Environ Assess Manag 2023;00:1–15. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

show abstract

A cognitive map framework to support integrated environmental assessment

Cited by 43 publications

References 35 publications

Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping

Transparency and Reproducibility in Participatory Systems Modelling: the Case of Fuzzy Cognitive Mapping

Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps

Water pollution from pharmaceutical use in livestock farming: Assessing differences between livestock types and production systems

Contact Info

Product

Resources

About