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The NewSTHEPS MetaData catalogue gives an overview of all persons and institutes involved and lists all created datasets and publications coming out of the project.

 

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Prevalence-adjusted optimisation of fuzzy models for species distribution
Mouton, A.M.; De Baets, B.; Van Broekhoven, E.; Goethals, P.L.M. (2009). Prevalence-adjusted optimisation of fuzzy models for species distribution. Ecol. Model. 220(15): 1776-1786. http://dx.doi.org/10.1016/j.ecolmodel.2009.04.020
In: Ecological Modelling. Elsevier: Amsterdam; Lausanne; New York; Oxford; Shannon; Tokyo. ISSN 0304-3800; e-ISSN 1872-7026
Peer reviewed article  

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Keyword
    Fresh water
Author keywords
    Performance criteria; Model evaluation; Model training; Stream; Fuzzy logic; Hill-climbing; Physical habitat; Expert knowledge; Adjusted average deviation; Kappa; CCI; TSS; Prevalence

Authors  Top 
  • Mouton, A.M.
  • De Baets, B.
  • Van Broekhoven, E.
  • Goethals, P.L.M.

Abstract
    Most performance criteria which have been applied to train ecological models focus on the accuracy of the model predictions. However, these criteria depend on the prevalence of the training set and often do not take into account ecological issues such as the distinction between omission and commission errors. Moreover, a previous study indicated that model training based on different performance criteria results in different optimised models. Therefore, model developers should train models based on different performance criteria and select the most appropriate model depending on the modelling objective. This paper presents a new approach to train fuzzy models based on an adjustable performance criterion, called the adjusted average deviation (aAD). This criterion was applied to develop a species distribution model for spawning grayling in the Aare River near Thun, Switzerland. To analyse the strengths and weaknesses of this approach, it was compared to model training based on other performance criteria. The results suggest that model training based on accuracy-based performance criteria may produce unrealistic models at extreme prevalences of the training set, whereas the aAD allows for the identification of more accurate and more reliable models. Moreover, the adjustable parameter in this criterion enables modellers to situate the optimised models in the search space and thus provides an indication of the ecological model relevance. Consequently, it may support modellers and river managers in the decision making process by improving model reliability and insight into the modelling process. Due to the universality and the flexibility of the approach, it could be applied to any other ecosystem or species, and may therefore be valuable to ecological modelling and ecosystem management in general.

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