Predicting insect outbreaks using machine learning: A mountain pine beetle case study.

Area of Science:

• Ecology and Environmental Science
• Forestry and Forest Management
• Computational Science and Machine Learning

Background:

• Accurate prediction of insect outbreaks, such as mountain pine beetle, is essential for effective forest management.
• Machine learning (ML) offers promising solutions for intermediate-term (e.g., 5-year) outbreak prediction.
• Challenges include selecting optimal ML models, relevant covariates (with time lags), and robust performance evaluation.

Purpose of the Study:

• To systematically identify and evaluate machine learning models for predicting mountain pine beetle outbreaks.
• To assess model performance for 1-, 3-, 5-, and 7-year future infestations in the Cypress Hills area.
• To investigate the impact of data splitting strategies and covariate history length on prediction accuracy.

Main Methods:

• Trained nine machine learning models, including generalized boosted regression trees (GBM) and novel mixed models.
• Compared random data splitting versus year-based splitting for training and testing datasets.
• Analyzed prediction accuracy in relation to covariate history length for various ML models.

Main Results:

• GBM models achieved 92% and 88% AUC for 1- and 3-year predictions; mixed models achieved 86% and 84% AUC for 5- and 7-year predictions.
• Year-based data splitting provided more accurate performance evaluations than random splitting, preventing misleadingly high scores.
• Neural network, naive Bayes, and GBM models showed improved accuracy with increased covariate history length, especially for shorter-term predictions.

Conclusions:

• Optimized machine learning models can reliably predict mountain pine beetle outbreaks across multiple time horizons.
• Appropriate data splitting and covariate selection are critical for accurate model evaluation and practical application.
• The developed predictive approach is adaptable for other invasive species and aids in forest pest management and field study planning.