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Discussion papers | Copyright
https://doi.org/10.5194/nhess-2018-85
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 03 Apr 2018

Research article | 03 Apr 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Evaluation of predictive models for post-fire debris flows occurrence in the western United States

Efthymios I. Nikolopoulos1, Elisa Destro2, Md Abul Ehsan Bhuiyan1, Marco Borga2, and Emmanouil N. Anagnostou1 Efthymios I. Nikolopoulos et al.
  • 1Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT, USA
  • 2Department of Leaf, Environment, Agriculture and Forestry, University of Padova, Legnaro, PD, Italy

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 new predictive model based on random forest algorithm is compared against current techniques for the prediction of post-fire debris flow occurrence in the western United States. The analysis is based on a database on post-fire debris flows recently published by 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, deems important but the choice of model used is shown to have a greater impact on the overall performance.

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Efthymios I. Nikolopoulos et al.
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Latest update: 16 Jul 2018
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Short summary
Debris flows, following wildfires, constitute a significant threat to downstream populations and infrastructure. Therefore, developing measures to reduce the vulnerability of local communities to debris flows is of paramount importance. This work proposes a new model for predicting post-fire debris flow occurrence at a regional scale and demonstrates that the proposed model has notably higher skill than the currently used approaches.
Debris flows, following wildfires, constitute a significant threat to downstream populations and...
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