A platform-independent fuzzy logic modeling framework for environmental decision support

Sheehan, Timothy C.; Gough, Mike

doi:10.1016/j.ecoinf.2016.05.001

Cited by 21 publications

(9 citation statements)

References 22 publications

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“…This classification generates reliability to the process that becomes standardized. Our model was able to describe the study area using quantitative (i.e., VDI and diversity of tree species) and qualitative (regeneration) variables because of the fuzzy logic that allows us to describe the variable flexibility; hence, it has been used to model landscapes and in different ecological and environmental studies (Sheehan and Gough, 2016).…”

Section: Discussionmentioning

confidence: 99%

A Fuzzy Logic Model for Zone Delineation in a Preservation Area in Brazil

Tambarussi¹,

Wilhelm²,

Magalhães³

et al. 2019

Appl. Ecol. Env. Res.

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Territorial planning plays a fundamental role in preservation of areas for ecological conservation and environmental management as well as in providing adequate spaces for visitation and research and prioritizing sustainable relationships between man and nature. The present work exhibits a methodological proposal that allows the standardization of territorial classifications in preservation areas. Based on fuzzy logic, the proposed model observes the particularities of a preservation area; hence, the decision generated in the classification of zones represents the work that is currently being developed in the areas of biology, geography, environmental engineering, among other research groups. The modeling combines the evaluation of the data collected in the field (variables: quantitative and qualitative) with vegetation indices obtained from satellite imagery. Combining this information enables each sampling point to be categorized, thereby generating preservation area zones. The results showed that the methodology can distinguish border areas as transition zones, allowing appropriate management to offer detailed information of the study area. The case study, in which the methodology was applied in a preservation area in southern Brazil, showed that the methodology assists in territorial planning, with a clear indication of what actions have been performed and simple data collection.

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Section: Discussionmentioning

confidence: 99%

A Fuzzy Logic Model for Zone Delineation in a Preservation Area in Brazil

Tambarussi¹,

Wilhelm²,

Magalhães³

et al. 2019

Appl. Ecol. Env. Res.

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“…The Environmental Evaluation Modeling System (EEMS) [38] is a fuzzy logic [39–40] modeling platform designed to inform answers to management questions. A model is represented by a logic tree, with each node corresponding to a displayable spatial layer or map (e.g.…”

Section: Methodsmentioning

confidence: 99%

A fuzzy logic decision support model for climate-driven biomass loss risk in western Oregon and Washington

Sheehan

Bachelet

2019

PLoS ONE

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Dynamic global vegetation model (DGVM) projections are often put forth to aid resource managers in climate change-related decision making. However, interpreting model results and understanding their uncertainty can be difficult. Sources of uncertainty include embedded assumptions about atmospheric CO2 levels, uncertain climate projections driving DGVMs, and DGVM algorithm selection. For western Oregon and Washington, we implemented an Environmental Evaluation Modeling System (EEMS) decision support model using MC2 DGVM results to characterize biomass loss risk. MC2 results were driven by climate projections from 20 General Circulation Models (GCMs) and Earth System Models (ESMs), under Representative Concentration Pathways (RCPs) 4.5 and 8.5, with and without assumed fire suppression, for three different time periods. We produced maps of mean, minimum, and maximum biomass loss risk and uncertainty for each RCP / +/- fire suppression / time period. We characterized the uncertainty due to RCP, fire suppression, and climate projection choice. Finally, we evaluated whether fire or climate maladaptation mortality was the dominant driver of risk for each model run. The risk of biomass loss generally increases in current high biomass areas within the study region through time. The pattern of increased risk is generally south to north and upslope into the Coast and Cascade mountain ranges and along the coast. Uncertainty from climate future choice is greater than that attributable to RCP or +/- fire suppression. Fire dominates as the driving factor for biomass loss risk in more model runs than mortality. This method of interpreting DGVM results and the associated uncertainty provides managers with data in a form directly applicable to their concerns and should prove helpful in adaptive management planning.

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“…where the approximation errors ∆h ij and ∆h ij are constants satisfying ∆h ij ≤ h ij (x(t))−ĥ ij (x(t)) ≤ ∆h ij for all x(t) or the domain of interest; Y ij (x(t)) ≥ 0 and Y ij (x(t)) ≥ Q ij (x(t)). From (27),V (x(t)) < 0 can be achieved if p i=1 c j=1 (ĥ ij (x(t))+∆h ij )Q ij (x(t))+ (∆h ij − ∆h ij )Y ij (x(t)) < 0, whereĥ ij (x(t)) can be any particular membership functions mentioned above. It can be seen that this stability condition contains the approximated membership functions, which is thus MFD.…”

Section: Approximated Membership Functionsmentioning

confidence: 99%

“…With the support of fuzzy set theory and mathematics, a fuzzy logic system can perform reasoning according to the designated linguistic rules. Fuzzy logic systems were used successfully in a wide range of areas and applications [2,3,4] such as assessment [5], classification [6,7,8], control [9,10,11], decision making [12,13,14,15,16,17], evaluation [18,19], forecasting [20,21,22], learning [23,24,25], modeling [26,27] and etc.…”

Section: Introductionmentioning

confidence: 99%