Abstract
Abstract. Rainfall-induced debris flows in recently burned mountainous areas cause significant economic losses and human casualties. Currently, prediction of post-fire debris flows is widely based on the use of power-law thresholds and logistic regression models. While these procedures have served with certain success in existing operational warning systems, in this study we investigate the potential to improve the efficiency of current predictive models with machine-learning approaches. Specifically, the performance of a predictive model based on the random forest algorithm is compared with current techniques for the prediction of post-fire debris flow occurrence in the western United States. The analysis is based on a database of post-fire debris flows recently published by the United States Geological Survey. Results show that predictive models based on random forest exhibit systematic and considerably improved performance with respect to the other models examined. In addition, the random-forest-based models demonstrated improvement in performance with increasing training sample size, indicating a clear advantage regarding their ability to successfully assimilate new information. Complexity, in terms of variables required for developing the predictive models, is deemed important but the choice of model used is shown to have a greater impact on the overall performance.
Highlights
Wildfires constitute a natural hazard with devastating consequences to the natural and built environment
An interesting point to note from these results is that for the smaller sample sizes examined (M = 100–500) the Random forest (RF)-ED performed marginally better than the RF-all but as the sample size increased, the situation is reversed and higher threat score (TS) values are associated with the RF-all model
We evaluated the performance of four different models for post-fire debris flow prediction in the western United States
Summary
Wildfires constitute a natural hazard with devastating consequences to the natural and built environment. In addition to the immediate impact of wildfire events to human lives, infrastructure and the environment, their adverse effects on landscape characteristics generate a cascade of hydrogeomorphic hazards (Shakesby and Doerr, 2006; Parise and Cannon, 2012; Diakakis et al, 2017). Post-fire debris flows (hereinafter DF) are predominantly derived from channel erosion and incision, usually generated during heavy precipitation events on burned areas (Cannon and DeGraff, 2009; Parise and Cannon, 2017). Recent studies have shown that in fire-affected regions the threat associated with debris flows may persist for several years after the fire incident (DeGraff et al, 2013; Diakakis et al, 2017), demonstrating the necessity for developing short and longterm plans for the mitigation of this hazard (DeGraff et al, 2013)
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