Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations
Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations
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DOI：
10.1016/j.ecolmodel.2018.08.011
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发表时间：
2018-11-24
影响因子：
3.1
通讯作者：
Wieland, Ralf
中科院分区：
文献类型：
--
作者：
Frueh, Linus;Kampen, Helge;Wieland, Ralf
我们测试了四种机器学习方法在与天气变量相关的蚊子物种发生分类中的性能：支持向量机，随机森林，逻辑回归和决策树。目的是找到一种方法，它显示了最准确的模型，用于预测潜在的地理分布的伊蚊，一种入侵的蚊子物种在德国。使用混淆矩阵的推导进行模型训练的评估。此外，我们引入了两个质量指标，“选择性”和“准确性”，用于评估的空间模拟，可视化通过Hasse图technique.从评估结果中，我们可以得出结论，一个特定的组合的两个到三个模型比一个单一的模型或模型的随机组合预测蚊子物种的潜在分布更好。
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.