Evaluation of machine learning methods for predicting eradication of aquatic invasive species

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Abstract

In the work, we evaluate the performance of machine learning approaches for predicting successful eradication of aquatic invasive species (AIS) and assess the extent to which eradication of an invasive species depends on the certain specified ecological features of the target ecosystem and/or features that characterize the planned intervention. We studied the outcomes of 143 planned attempts for eradicating AIS, where each attempt was described by ecological and eradication-strategy-related features of the target ecosystem. We considered several machine learning approaches to determine whether one could produce a classifier that accurately predicts weather an invasive species will be eradicated. To assess each learner’s performance, we examined its tenfold cross-validated prediction accuracy as well as the false positive rate, the F-measure, and the Area Under the ROC Curve. We also used Kaplan–Meier survival analysis to determine which features are relevant to predicting the time required for each eradication program. Across the five typical machine learning approaches, our analysis suggests that learners trained by the decision tree work well, and have the best performance. In particular, by examining the trained decision tree model, we found that if an occupied area was not large and/or containments of AIS dispersal were employed, the eradication of AIS was likely to be successful. We also trained decision tree models over only the ecological features and found that their performances were comparable with that of models trained using all features. As our trained decision tree models are accurate, decision makers can use them to estimate the result of the proposed actions before they commit to which specific strategy should be applied.

Keywords

Aquatic species Machine learning Survival analysis Ecological features Planned intervention 
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Notes

Acknowledgements

MAL acknowledges support from a Canadian Research Chair, an NSERC Discovery Grant and a Killiam Research Fellowship. RG acknowledges support from NSERC and AMII. YX acknowledges support from the Simon foundations. We thank Boris Beric, David Drolet and Huge MacIsaac for their contribution on data collection and useful comments. This work was partially supported by the Alberta Innovates Centre for Machine Learning, the Canadian Aquatic Invasive Species Network, the Natural Sciences and Engineering Research Council of Canada.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Mathematical SciencesUniversity of CincinnatiCincinnatiUSA
  2. 2.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  3. 3.Center for Mathematical Biology, Department of Mathematical and Statistical SciencesUniversity of AlbertaEdmontonCanada
  4. 4.Department of Biological SciencesUniversity of AlbertaEdmontonCanada

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