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

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

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.

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  • Früh, Linus & Kampen, Helge & Kerkow, Antje & Schaub, Günter A. & Walther, Doreen & Wieland, Ralf, 2018. "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations," Ecological Modelling, Elsevier, vol. 388(C), pages 136-144.
    • Handle: RePEc:eee:ecomod:v:388:y:2018:i:c:p:136-144
    DOI: 10.1016/j.ecolmodel.2018.08.011
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    References listed on IDEAS

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    Cited by:

    1. Wieland, Ralf & Kuhls, Katrin & Lentz, Hartmut H.K. & Conraths, Franz & Kampen, Helge & Werner, Doreen, 2021. "Combined climate and regional mosquito habitat model based on machine learning," Ecological Modelling, Elsevier, vol. 452(C).
    2. Kerkow, Antje & Wieland, Ralf & Gethmann, Jörn M. & Hölker, Franz & Lentz, Hartmut H.K., 2022. "Linking a compartment model for West Nile virus with a flight simulator for vector mosquitoes," Ecological Modelling, Elsevier, vol. 464(C).
    3. Damiana Ravasi & Francesca Mangili & David Huber & Laura Azzimonti & Lukas Engeler & Nicola Vermes & Giacomo Del Rio & Valeria Guidi & Mauro Tonolla & Eleonora Flacio, 2022. "Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito Aedes albopictus in Switzerland," IJERPH, MDPI, vol. 19(6), pages 1-22, March.

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