Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire

To develop effective wildfire evacuation plans, it is crucial to study evacuation decision-making and identify the factors affecting individuals’ choices. Statistic models (e.g., logistic regression) are widely used in the literature to predict household evacuation decisions, while the potential of machine learning models has not been fully explored. This study compared seven machine learning models with logistic regression to identify which approach is better for predicting a householder’s decision to evacuate. The machine learning models tested include the naïve Bayes classifier, K-nearest neighbors, support vector machine, neural network, classification and regression tree (CART), random forest, and extreme gradient boosting. These models were calibrated using the survey data collected from the 2019 Kincade Fire. The predictive performance of the machine learning models and the logistic regression was compared using F1 score, accuracy, precision, and recall. The results indicate that all the machine learning models performed better than the logistic regression. The CART model has the highest F1 score among all models, with a statistically significant difference from the logistic regression model. This CART model shows that the most important factor affecting the decision to evacuate is pre-fire safety perception. Other important factors include receiving an evacuation order, household risk perception (during the event), and education level.