ArticleEcology and evolution2021
Predicting insect outbreaks using machine learning: A mountain pine beetle case study.
Article in Ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Evolution of Modeling Approaches: From Statistical Models to Deep Learning for Locust and Grasshopper Forecasting.Insects · 2026Review
- Comparing literature-guided, LLM-assisted, and data-driven models for predicting angler pressure.PloS one · 2026Article
- Article
- Biological Barriers to Forest Pest Invasions: A Novel Host Tree Slows Mountain Pine Beetle Range Expansion.Ecology and evolution · 2025Article
- Machine Learning Using Template-Based-Predicted Structure of Haemagglutinin Predicts Pathogenicity of Avian Influenza.Journal of microbiology and biotechnology · 2024Article
- Modeling Tick Populations: An Ecological Test Case for Gradient Boosted Trees.bioRxiv : the preprint server for biology · 2023Article
- TinyML-Sensor for Shelf Life Estimation of Fresh Date Fruits.Sensors (Basel, Switzerland) · 2023Article
- From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination.Bulletin of mathematical biology · 2022Article
- A Hypothesis-Free Bridging of Disease Dynamics and Non-pharmaceutical Policies.Bulletin of mathematical biology · 2022Article
- Predicting insect outbreaks using machine learning: A mountain pine beetle case study.Ecology and evolution · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Planning forest management relies on predicting insect outbreaks such as mountain pine beetle, particularly in the intermediate-term future, e.g., 5-year. Machine-learning algorithms are potential solutions to this challenging problem due to their many successes across a variety of prediction tasks. However, there are many subtle challenges in applying them: identifying the best learning models and the best subset of available covariates (including time lags) and properly evaluating the models to avoid misleading performance-measures. We systematically address these issues in predicting the chance of a mountain pine beetle outbreak in the Cypress Hills area and seek models with the best performance at predicting future 1-, 3-, 5- and 7-year infestations. We train nine machine-learning models, including two generalized boosted regression trees (GBM) that predict future 1- and 3-year infestations with 92% and 88% AUC, and two novel mixed models that predict future 5- and 7-year infestations with 86% and 84% AUC, respectively. We also consider forming the train and test datasets by splitting the original dataset
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.