Using fuzzy set theory to address the uncertainty of susceptibility to drought

Eierdanz, Frank; Alcamo, Joseph; Acosta, Lilibeth A.; Krömker, Dörthe; Tänzler, Dennis

doi:10.1007/s10113-008-0069-1

Cited by 29 publications

(16 citation statements)

References 23 publications

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“…The twelve indicators were first aggregated into the six determinants of adaptive capacity, which were further aggregated into the three components, and then finally aggregated into the adaptive capacity index (see Fig. 2) using the method of Cornelissen et al (2001) and Eierdanz et al (2008). This process involved the sequential application of fuzzification, fuzzy inference and defuzzification, which was carried out using the Fuzzy Logic Toolbox of MathWorks Inc (2000).…”

Section: Aggregation Of Indicatorsmentioning

confidence: 99%

“…Details on the cluster analysis using the k-means approach and the membership functions of the indicators are provided in Annex 2. Fuzzy inference involves the application of inference rules, which are designed from experience, expert knowledge and literature sources (Eierdanz et al, 2008). These rules are ''if-then'' statements combining the qualitative values of two or more input variables.…”

Section: Aggregation Of Indicatorsmentioning

confidence: 99%

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A spatially explicit scenario-driven model of adaptive capacity to global change in Europe

Acosta

Klein

Reidsma³

et al. 2013

Global Environmental Change

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Section: Aggregation Of Indicatorsmentioning

confidence: 99%

Section: Aggregation Of Indicatorsmentioning

confidence: 99%

A spatially explicit scenario-driven model of adaptive capacity to global change in Europe

Acosta

Klein

Reidsma³

et al. 2013

Global Environmental Change

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“…Fuzzy models can apply expert knowledge (e.g., heuristic rules) to ecological data for inferring solutions and solving complex problems (Mackinson 2001;Shepard 2005). The knowledge at the core of the model can be described in natural language and fuzzy set theory can handle the uncertainties associated with this natural language (Eierdanz et al 2008). Thus, even though the inference model and its outputs are numerical, the core of the model is qualitative and its empirical structure can be explained easily to policymakers or other stakeholders (Mackinson 2001;Reynolds et al 2003).…”

Section: Model: Dispersal Rulesmentioning

confidence: 99%

Climate change and plant dispersal along corridors in fragmented landscapes of Mesoamerica

Imbach

Locatelli

Molina

et al. 2013

Ecology and Evolution

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Climate change is a threat to biodiversity, and adaptation measures should be considered in biodiversity conservation planning. Protected areas (PA) are expected to be impacted by climate change and improving their connectivity with biological corridors (BC) has been proposed as a potential adaptation measure, although assessing its effectiveness remains a challenge. In Mesoamerica, efforts to preserve the biodiversity have led to the creation of a regional network of PA and, more recently, BC. This study evaluates the role of BC for facilitating plant dispersal between PA under climate change in Mesoamerica. A spatially explicit dynamic model (cellular automaton) was developed to simulate species dispersal under different climate and conservation policy scenarios. Plant functional types (PFT) were defined based on a range of dispersal rates and vegetation types to represent the diversity of species in the region. The impacts of climate change on PA and the role of BC for dispersal were assessed spatially. Results show that most impacted PA are those with low altitudinal range in hot, dry, or high latitude areas. PA with low altitudinal range in high cool areas benefit the most from corridors. The most important corridors cover larger areas and have high altitude gradients. Only the fastest PFT can keep up with the expected change in climate and benefit from corridors for dispersal. We conclude that the spatial assessment of the vulnerability of PA and the role of corridors in facilitating dispersal can help conservation planning under a changing climate.

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“…iv)Defuzzification (Bothe 1998;Aliev et al 2000) Defuzzification was used to combine the results of each rule into one unique quantitative result. The researcher used the defuzzification technique called center of gravity (Eierdanz et al 2008).The fuzzy set membership function had the graph of a triangle. As done by Eierdanz et al (2008) this formed a trapezoid.…”

Section: Data Analysis Methodsmentioning

confidence: 99%

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

2020

FSQM

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According to IPCC, (2007) Vulnerability is the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and extremes. It is a function of exposure, sensitivity, and adaptive capacity (Marshall et al. 2010; Wongbusarakum and Loper 2011, Mucke 2012). The study sought to determine the vulnerability to climate related shocks among smallholder farmers in Kinakomba Ward. The main purpose of the study was to assess the extent to which, exposure, sensitivity and adaptive capacity contributed to the vulnerability of the smallholder farmers on their livelihoods. The study was carried out in Kinakomba ward in Tana River County, Kenya. A descriptive survey research design was used. Stratified random sampling was employed to select 390 out of a population of 3,908 households. Data was collected using questionnaires and Focus Group Discussions. Descriptive statistics, metric of sensitivity, composite index was used to analyse sensitivity. Two methods were used to analyse exposure. Firstly the fuzzy logic in assessing susceptibility to drought involving a selection of input variables, Fuzzification, inference modelling and defuzzification and secondly DrinC Model software. The researcher used two methods to analyse adaptive capacity namely the interview with 390 farming households to gather data on farming and household characteristics and natural resources availability and secondly a panel of 15 Key Informants provided ratings of indicators of adaptive capacity using analytic hierarchy process (AHP). To understand the interaction of these variables this study Analysed a quantitative Vulnerability score by using an equation which combined three contributing indices of exposure, sensitivity and adaptive capacity each normalized to 0-1 Scale (Adger and Vincent, 2005;Allison et al. 2009), the vulnerability score was obtained by adding exposure to sensitivity and subtracting the adaptive capacity. The key results from the study showed that exposure (p=0.000066) and sensitivity (P=0.00038) had a significant effect on livelihoods. These factors were also found to have a negative influence on livelihoods in the area. Further statistical findings showed that as adaptive capacity increased vulnerability decreased while when sensitivity increased vulnerability increased at the same time. The study concluded that these two dimensions of vulnerability could be modified by policy and development. The study also concluded that exposure contributed more than sensitivity and adaptive capacity to the cumulative vulnerability. The study recommended that the County and National governments and stakeholders to employ measures to adapt to climate change and variability. This study also recommended that the Government in partnership with other stakeholders develop a comprehensive disaster risk management framework to address the drought hazards and undertake mitigation and adaptation measures by equipping the smallholder farmers with knowledge on how to cope with the cyclic...

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Using fuzzy set theory to address the uncertainty of susceptibility to drought

Cited by 29 publications

References 23 publications

A spatially explicit scenario-driven model of adaptive capacity to global change in Europe

A spatially explicit scenario-driven model of adaptive capacity to global change in Europe

Climate change and plant dispersal along corridors in fragmented landscapes of Mesoamerica

Vulnerability of Smallholder Farmers to Climate Related Shocks in Kinakomba Ward, Tana River County, Kenya

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