Document Type
Article
Publication Date
1-10-2019
Department
Department of Mathematical Sciences; Department of Geological and Mining Engineering and Sciences
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.
Publication Title
Geomatics, Natural Hazards and Risk
Recommended Citation
Addison, P.,
Oommen, T.,
&
Sha, Q.
(2019).
Assessment of post-wildfire debris flow occurrence using classifier tree.
Geomatics, Natural Hazards and Risk,
10(1), 505-518.
http://doi.org/10.1080/19475705.2018.1530306
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/441
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
Copyright 2019 The Author(s). Article deposited here in compliance with publisher policies. Publisher's version of record: https://doi.org/10.1080/19475705.2018.1530306