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

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Abstract

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.

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  1. Risk perception (and other ‘latent’ variables) are not necessarily an independent variable per se, but instead, a mediator variable when estimating evacuation decisions and movement. In future work, we hope to create a model which is ‘dependent’ only on the factors that can be obtained from any given affected area (e.g., population, environmental, and place-based variables)—to then model threat and risk perceptions and then the evacuation decision.

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Acknowledgements

This research was supported by the Natural Hazards Center Quick Response Research Program. The Quick Response program is based on work supported by the National Science Foundation (Award #1635593). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF or the Natural Hazards Center. The authors would like to thank the many residents of Sonoma County, California for sharing their experiences with us for this study.

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Authors and Affiliations

  1. Department of Civil and Coastal Engineering, University of Florida, 1949 Stadium Rd, Gainesville, FL, 32611, USA

    Ningzhe Xu & Xilei Zhao

  2. Department of Civil, Construction and Environmental Engineering, The University of Alabama, Tuscaloosa, USA

    Ningzhe Xu

  3. School of Built Environment, Massey University, Auckland, New Zealand

    Ruggiero Lovreglio

  4. School of Engineering, RMIT University, Melbourne, Australia

    Erica D. Kuligowski

  5. Department of Geography, University of Utah, Salt Lake City, USA

    Thomas J. Cova

  6. Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch, New Zealand

    Daniel Nilsson

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Appendices

Appendix A: The Results of Logistic Regression

See Table 4

Table 4 The Results of Logistic Regression
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Appendix B: The Results of Machine Learning Models

See Table 5

Table 5 Variable Importance for Each Machine Learning Model (top 10)
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Appendix C: Paired t-test Results with the Bonferroni Correction

See Tables 6, 7

Table 6 Paired t-test Results (p-value) of F1 Score with the Bonferroni Correction (i.e., Correcting p-value by Multiplying the Number of Comparisons)
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Table 7 Paired t-test Results (p-value) of Accuracy with the Bonferroni Correction (i.e., Correcting p-value by Multiplying the Number of Comparisons)
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Appendix D: Source Code

The model comparison and paired t-test programs were implemented in R and open-sourced on GitHub (https://github.com/EvacuationBehavior/ML-for-Modeling-Wildfire-Eva-DM).

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Xu, N., Lovreglio, R., Kuligowski, E.D. et al. Predicting and Assessing Wildfire Evacuation Decision-Making Using Machine Learning: Findings from the 2019 Kincade Fire. Fire Technol 59, 793–825 (2023). https://doi.org/10.1007/s10694-023-01363-1

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