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Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations

Linus Früh, Helge Kampen, Antje Kerkow, Günter A. Schaub, Doreen Walther and Ralf Wieland

Ecological Modelling, 2018, vol. 388, issue C, 136-144

Abstract: We tested four machine learning methods for their performance in the classification of mosquito species occurrence related to weather variables: support vector machine, random forest, logistic regression and decision tree. The objective was to find a method which showed the most accurate model for the prediction of the potential geographical distribution of Aedes japonicus japonicus, an invasive mosquito species in Germany.

Keywords: Decision tree; Logistic regression; Random forest; Support vector machine; Hasse diagram technique; Aedes japonicus japonicus (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:ecomod:v:388:y:2018:i:c:p:136-144