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

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 certai...

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Bibliographic Details
Published in:Natural hazards and earth system sciences 2018-09, Vol.18 (9), p.2331-2343
Main Authors: Nikolopoulos, Efthymios I, Destro, Elisa, Bhuiyan, Md Abul Ehsan, Borga, Marco, Anagnostou, Emmanouil N
Format: Article
Language:English
Subjects:
Ash (Chemistry)
Casualties
Climate change
Debris flow
Detritus
Distribution
Economic impact
Environmental aspects
Fires
Geological surveys
Learning algorithms
Machine learning
Mathematical models
Mountain regions
Mountainous areas
Performance evaluation
Performance prediction
Prediction models
Procedures
Rain
Rainfall
Regression analysis
Regression models
Studies
Surveying
Training
Warning systems
Western United States
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Summary: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.
ISSN:1684-9981
1561-8633
1684-9981
DOI:10.5194/nhess-18-2331-2018