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

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

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. The evaluation of the model trainings was conducted using derivations of a confusion matrix. Furthermore, we introduced two quality indices, ‘selectivity’ and ‘exactness’, for the evaluation of the spatial simulation, visualised through the Hasse diagram technique. From the evaluation results we can conclude that a specific combination of two to three models performs better in predicting the potential distribution of the mosquito species than a single model or the random combination of models.

Vorschau

Zitieren

Zitierform:

Früh, Linus / Kampen, Helge / Kerkow, Antje / et al: Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations. 2018.

Rechte

Nutzung und Vervielfältigung:

Export