scholarly journals Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

2016 ◽  
Author(s):  
Dennis M. Staley ◽  
Jacquelyn A. Negri ◽  
Jason W. Kean ◽  
Jayme L. Laber ◽  
Anne C. Tillery ◽  
...  
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Debris Flow ◽  
Western United States ◽  
Regression Equations ◽  
Fire Debris

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

2018 ◽  
Vol 18 (9) ◽  
pp. 2331-2343 ◽  
Author(s):  
Efthymios I. Nikolopoulos ◽  
Elisa Destro ◽  
Md Abul Ehsan Bhuiyan ◽  
Marco Borga ◽  
Emmanouil N. Anagnostou
Keyword(s):  
United States ◽  
Random Forest ◽  
Debris Flow ◽  
Predictive Models ◽  
Debris Flows ◽  
The United States ◽  
Western United States ◽  
Economic Losses ◽  
United States Geological Survey ◽  
Fire Debris

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.

ESTIMATING POST-FIRE DEBRIS-FLOW HAZARDS IN THE WESTERN UNITED STATES PRIOR TO WILDFIRE

2017 ◽  
Author(s):  
Dennis M. Staley ◽  
◽  
Jason W. Kean ◽  
Luke McGuire ◽  
Francis K. Rengers ◽  
...  
Keyword(s):  
United States ◽  
Debris Flow ◽  
Western United States ◽  
Fire Debris

Prediction of spatially explicit rainfall intensity–duration thresholds for post-fire debris-flow generation in the western United States

Geomorphology ◽  
2017 ◽  
Vol 278 ◽  
pp. 149-162 ◽  
Author(s):  
Dennis M. Staley ◽  
Jacquelyn A. Negri ◽  
Jason W. Kean ◽  
Jayme L. Laber ◽  
Anne C. Tillery ◽  
...  
Keyword(s):  
United States ◽  
Debris Flow ◽  
Rainfall Intensity ◽  
Western United States ◽  
Spatially Explicit ◽  
Flow Generation ◽  
Fire Debris

Minimizing economic impacts from post-fire debris flows in the western United States

2016 ◽  
Vol 83 (1) ◽  
pp. 149-176 ◽  
Author(s):  
Kevin McCoy ◽  
Vitaliy Krasko ◽  
Paul Santi ◽  
Daniel Kaffine ◽  
Steffen Rebennack
Keyword(s):  
United States ◽  
Debris Flows ◽  
Economic Impacts ◽  
Western United States ◽  
Fire Debris

The Timing of Susceptibility to Post-Fire Debris Flows in the Western United States

2015 ◽  
Vol 21 (4) ◽  
pp. 277-292 ◽  
Author(s):  
JEROME V. DeGRAFF ◽  
SUSAN H. CANNON ◽  
JOSEPH E. GARTNER
Keyword(s):  
United States ◽  
Debris Flows ◽  
Western United States ◽  
Fire Debris

Machine Learning Based Predictive Modeling of Debris Flow Probability Following Wildfire in the Intermountain Western United States

2017 ◽  
Vol 49 (6) ◽  
pp. 717-735 ◽  
Author(s):  
Ashley N. Kern ◽  
Priscilla Addison ◽  
Thomas Oommen ◽  
Sean E. Salazar ◽  
Richard A. Coffman
Keyword(s):  
United States ◽  
Machine Learning ◽  
Debris Flow ◽  
Predictive Modeling ◽  
Western United States

Comment on “Estimating canopy fuel characteristics in five conifer stands in the western United States using tree and stand measurements”Appears in Can. J. For. Res. 36: 2803–2814 (2006).

2010 ◽  
Vol 40 (11) ◽  
pp. 2262-2263 ◽  
Author(s):  
Miguel G. Cruz ◽  
Martin E. Alexander ◽  
Ronald H. Wakimoto
Keyword(s):  
United States ◽  
Wildland Fire ◽  
Input Parameter ◽  
Coniferous Forest ◽  
Western United States ◽  
Regression Equations ◽  
Forest Fuel ◽  
Fuel Characteristics ◽  
Stand Height

Reinhardt et al. (E. Reinhardt, J. Scott, K. Gray, and R. Keane, Can. J. For. Res. 36: 2803–2814, 2006) questioned the validity of the regression equations for estimating canopy base heights in coniferous forest fuel types developed by Cruz et al. (M.G. Cruz, M.E. Alexander, and R.H. Wakimoto, Int. J. Wildland Fire, 12: 39–50, 2003) to produce logical results when applied to simulations involving low thinning. This turns out to be an error in interpretation with regard to the stand height input parameter.

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