Assessment of post-wildfire debris flow occurrence using classifier tree

Abstract

Besides the dangers of an actively burning wildfire, a plethora of other hazardous consequences can occur afterwards. Debris flows are among the most hazardous of these, being known to cause fatalities and extensive damage to infrastructure. Although debris flows are not exclusive to fire affected areas, a wildfire can increase a location’s susceptibility by stripping its protective covers like vegetation and introducing destabilizing factors such as ash filling soil pores to increase runoff potential. Due to the associated dangers, researchers are developing statistical models to isolate susceptible locations. Existing models predominantly employ the logistic regression algorithm; however, previous studies have shown that the relationship between the predictors and response is likely better predicted using nonlinear modeling. We therefore propose the use of nonlinear C5.0 decision tree algorithm, a simple yet robust algorithm that uses inductive inference for categorical data modelling. It employs a tree-like decision making system that makes conditional statements to split data into homogeneous classes. Our results showed the C5.0 approach to produce stable and higher validation metrics in comparison to the logistic regression. A sensitivity of 81% and specificity of 78% depicts improved predictive capability and gives credence to the hypothesis that data relationships are likely nonlinear.

Keywords:

• Burn severity
• debris flow
• decision tree
• hazard assessment
• machine learning

Acknowledgments

The authors would also like to thank the following individual and institution for their contributions and roles in making this work possible: Caesar Singh, USDOT program manager and the USGS for making the data freely available online.

Disclaimer

The views, opinions, findings, and conclusions reflected in this paper are the responsibility of the authors only and do not represent the official policy or position of the USDOT/OST-R or any State or other entity.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This project was funded by the US Department of Transportation (USDOT) through the Office of the Assistant Secretary for Research and Technology,USDOT Cooperative Agreement No. OASRTRS-14-H-UARK.