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

  • Author(s) / Creator(s)
  • 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 crossvalidated
    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

  • Date created
  • Subjects / Keywords
  • Type of Item
    Article (Draft / Submitted)
  • DOI
  • License
    Attribution-NonCommercial 4.0 International