2005
DOI: 10.1051/kmae:2005029
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Development of a Habitat Suitability Index for the Noble Crayfish Astacus Astacus Using Fuzzy Modelling

Abstract: A Geographic Information System (GIS) and fuzzy modelling were used to develop a habitat suitability index for the noble crayfish, Astacus astacus. The model is based on crayfish distribution data for the federal state Hesse, Germany, which had been recorded between 1988 and 1996. It includes 185 sites with noble crayfish in 126 watercourses. Official data on the morphological quality of surface waters recorded between 1996 and 1998 by order of the Ministry of Hesse for Environment, Rural Areas and Consumers P… Show more

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Cited by 6 publications
(5 citation statements)
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“…There are many different ways in machine learning involving patterns in which relationships will be discovered, each of which determines the type of technique that can be used to make sense of the output from the data. The most commonly used machine learning methods in the literature are; artificial neural networks (Surayanarayana et al, 2008;Tirelli et al, 2011;Benzer et al, 2017), logistic regression (Benzer et al, 2017;Gültepe and Gültepe, 2020), fuzzy modeling (Zuther et al, 2005), genetic algorithms and programming (Luna et al, 2019), decision tree (Tirelli et al, 2011;Gültepe and Gültepe, 2020), Bayesian network approach (Lin et al, 2004;Hamilton et al, 2015;Trifonova et al, 2017), random forest (Gültepe and Gültepe, 2020;Adibi et al, 2020), support vector machine (Gültepe and Gültepe, 2020;Favaro et al, 2011). Regression-based modeling techniques are widely used to estimate species distribution.…”
Section: Introductionmentioning
confidence: 99%
“…There are many different ways in machine learning involving patterns in which relationships will be discovered, each of which determines the type of technique that can be used to make sense of the output from the data. The most commonly used machine learning methods in the literature are; artificial neural networks (Surayanarayana et al, 2008;Tirelli et al, 2011;Benzer et al, 2017), logistic regression (Benzer et al, 2017;Gültepe and Gültepe, 2020), fuzzy modeling (Zuther et al, 2005), genetic algorithms and programming (Luna et al, 2019), decision tree (Tirelli et al, 2011;Gültepe and Gültepe, 2020), Bayesian network approach (Lin et al, 2004;Hamilton et al, 2015;Trifonova et al, 2017), random forest (Gültepe and Gültepe, 2020;Adibi et al, 2020), support vector machine (Gültepe and Gültepe, 2020;Favaro et al, 2011). Regression-based modeling techniques are widely used to estimate species distribution.…”
Section: Introductionmentioning
confidence: 99%
“…There are many different ways in machine learning involving patterns in which relationships will be discovered, each of which determines the type of technique that can be used to make sense of the output from the data. The most commonly used machine learning methods in the literature are; artificial neural networks [68], logistic regression [8–9], fuzzy modeling [10], genetic algorithms and programming [11], decision tree [7,9], Bayesian network approach [1214], random forest [9,15], support vector machine [9,16]. Regression-based modeling techniques are widely used to estimate species distribution.…”
Section: Introductionmentioning
confidence: 99%
“…The most commonly used machine learning methods in the literature are; artificial neural networks [6][7][8], logistic regression [8][9], fuzzy modeling [10], genetic algorithms and programming [11],…”
Section: Introductionmentioning
confidence: 99%
“…Fuzzy set methodology has the potential to provide better land evaluations than the commonly used discrete approaches because this approach can accommodate the effects of attribute values and properties that are close to category boundaries (Stoms et al, 2002). A number of fuzzy MCDA approaches that combine the advantages of these two methods have been developed for assessing land suitability (Ruger et al, 2005;Sicat et al, 2005;Zuther et al, 2005). In addition, Mendas & Delali (2012) developed a spatial decisionsupport system that enabled the preparation of agricultural land-use suitability maps by integrating GIS with the ELECTRE Tri multi-criterion analysis method.…”
Section: Introductionmentioning
confidence: 99%