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\hypersetup{pdftitle={The Development of Classification Trees (J48) for the Prediction of the Habitat Suitability of the Roach in the River Basin in Flanders (Belgium)},pdfauthor={Rahmat Zarkami},pdfsubject={Tropentag 2010: Abstract},pdfkeywords={classification trees, ecological modelling, Flanders, habitat suitability, river basins, roach},pdfpagemode=None,colorlinks=true}
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\parbox[b]{13.4cm}{\centering \large{\textbf{Tropentag, September 14-16, 2010, Zurich}}\\[1ex] \Large{``World Food System  ---\\A Contribution from Europe''\\[2ex]}}
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\Large{\textbf{The Development of Classification Trees (J48) for the Prediction of the Habitat Suitability of the Roach in the River Basin in Flanders (Belgium)\footnote{\textbf{Contact Address:} Rahmat Zarkami, Guilan University, Environmental Science,  Faculty of Natural Resources University of Guilan, 1144~Sowmeh Sara, Iran, \mbox{e-mail}: \email{rzarkami2002@yahoo.co.uk}}\\[0.8ex]}}
\normalsize{\textsc{Rahmat Zarkami}}
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\begin{itemize*}
\item[]{\small{\textit{Guilan University, Environmental Science, Iran}}}
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\index[author]{Zarkami, Rahmat}
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\textbf{Abstract}
\begin{abstract}
\normalsize{

The main aim of this study was to predict the habitat suitability of roach (Rutilus rutilus L.) by the classification tree methods (J48). To achieve this, the J48 algorithm was applied to 594 dataset gathered in 377 sampling sites in several river basins in Flanders (Belgium)over the historical period 1993--2004. Dataset consisted of a combination of structural"=habitat and physical"=chemical variables (used as independent variables) and biological variables (merely based on presence/absence and abundance data that could serve as dependent variables). The reliability of classification tree methods was assessed based on two model performances which were a combination of Correctly Classified Instances and Cohen's Kappa. To find the best model performance, a three"=fold cross validation was tested on the entire dataset. So as to assess the model stability, the dataset were remixed in 5 times. In that case, in total 15 different model training and validation sets were obtained for three subsets. The effect of 4 PCFs (pruning confidence factors) was examined on the reliability and model complexity in each subset. Due to the ease of the interpretation of the results, only the fifth remix out of 5 remixes were selected that had the best predictive performance for the prediction of habitat requirements of roach. On the basis of the obtained results, the induced model could predict well the presence/absence of roach in river basins in Flanders so that the highest overall mean of CCI and Kappa in the given remix proved that more than \mbox{80\,\%} of instances were correctly classified. When analysing the ecological relevance of classification tree methods, it seems that the structural"=habitat variables in particular distance from the source and width were more important predictors relative to the physical"=chemical ones for predicting habitat suitability of roach in the river basins in Flanders. 







}

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\noindent \textbf{Keywords:} classification trees, ecological modelling, Flanders, habitat suitability, river basins, roach
\index[key]{Ecological modelling}
\index[key]{Classification trees}
\index[key]{Flanders}
\index[key]{Habitat suitability}
\index[key]{River basins}
\index[key]{Roach}
\end{document}