Articles | Volume 18, issue 9
https://doi.org/10.5194/nhess-18-2331-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-18-2331-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Sep 2018
Research article |
| 04 Sep 2018
| 04 Sep 2018
Evaluation of predictive models for post-fire debris flow occurrence in the western United States
Efthymios I. Nikolopoulos
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of
Connecticut, Storrs, CT, USA
Elisa Destro
Department of Leaf, Environment, Agriculture and Forestry, University
of Padova, Legnaro, PD, Italy
Md Abul Ehsan Bhuiyan
Department of Civil and Environmental Engineering, University of
Connecticut, Storrs, CT, USA
Marco Borga
Department of Leaf, Environment, Agriculture and Forestry, University
of Padova, Legnaro, PD, Italy
Emmanouil N. Anagnostou
Department of Civil and Environmental Engineering, University of
Connecticut, Storrs, CT, USA
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Cited
27 citations as recorded by crossref.
- 四川雅江县火后泥石流易发性与冲出规模预测模型 X. Gong et al. https://doi.org/10.3799/dqkx.2025.094
- A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data E. Orland et al. https://doi.org/10.1029/2022GL099850
- Debris Flow Prediction Based on the Fast Multiple Principal Component Extraction and Optimized Broad Learning G. Xu et al. https://doi.org/10.3390/w14213374
- Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis L. Yang et al. https://doi.org/10.3390/w16070923
- Early post‐fire debris‐flow initiation following the March 2024 Yajiang Fire, Sichuan, China Y. Zhou et al. https://doi.org/10.1002/esp.70380
- Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province J. Xiong et al. https://doi.org/10.3390/ijgi9020133
- A multi-algorithm fusion approach for identifying post-earthquake rainfall-induced debris flow catchments H. Yin et al. https://doi.org/10.1007/s10064-025-04495-6
- Advanced wind speed prediction using convective weather variables through machine learning application B. Md Abul Ehsan et al. https://doi.org/10.1016/j.acags.2019.100002
- Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.123957
- Weather-related thresholds for wildfire danger in a Mediterranean region: The case of Greece K. Papagiannaki et al. https://doi.org/10.1016/j.agrformet.2020.108076
- Spatial clustering-based method for Italian marginal areas toward the sustainable regeneration M. Pignatelli et al. https://doi.org/10.48264/VVSIEV-20233207
- Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective T. Jin et al. https://doi.org/10.3390/rs14061306
- Spatiotemporal analysis of weather-related fire danger associated with climate change in the Zagros Mountains, Iran G. Roshan et al. https://doi.org/10.1007/s00477-024-02850-9
- Susceptibility assessment of “2020.3.30” Xichang post-fire debris flow using a machine learning method T. Jin et al. https://doi.org/10.1088/1755-1315/861/6/062039
- Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment J. Hirschberg et al. https://doi.org/10.5194/nhess-21-2773-2021
- A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods M. Kazemi et al. https://doi.org/10.1007/s12145-021-00570-0
- Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA L. McGuire et al. https://doi.org/10.5194/nhess-24-1357-2024
- Evaluating the thresholds for predicting post-earthquake debris flows: Comparison of meteorological, hydro-meteorological and critical discharge approaches Z. Wei et al. https://doi.org/10.1016/j.enggeo.2024.107773
- Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China Y. Zhang et al. https://doi.org/10.3390/rs11232801
- Применение модели логистической регрессии при принятии решений по определению количества привлекаемых сил на ликвидацию лесных пожаров Д. Медведев et al. https://doi.org/10.22227/0869-7493.2024.33.04.84-96
- Machine learning prediction of post-earthquake debris flows incorporating hydrological variables X. Wang et al. https://doi.org/10.1007/s11069-026-07970-9
- Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation M. Diakakis et al. https://doi.org/10.3390/land12030555
- Evaluation of a fire safety risk prediction model for an existing building W. Rzaij & B. Al-Obaidi https://doi.org/10.1515/jmbm-2022-0007
- Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing M. Choi & S. Jun https://doi.org/10.3390/app10124199
- Predicting post-wildfire debris flow onset using machine learning models on multi-parameter experimental data M. Movasat & I. Tomac https://doi.org/10.1007/s43503-026-00094-w
- Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. https://doi.org/10.5194/nhess-22-2317-2022
- Minimal effect of prescribed burning on fire spread rate and intensity in savanna ecosystems A. Moustakas & O. Davlias https://doi.org/10.1007/s00477-021-01977-3
27 citations as recorded by crossref.
- 四川雅江县火后泥石流易发性与冲出规模预测模型 X. Gong et al. https://doi.org/10.3799/dqkx.2025.094
- A Scalable Framework for Post Fire Debris Flow Hazard Assessment Using Satellite Precipitation Data E. Orland et al. https://doi.org/10.1029/2022GL099850
- Debris Flow Prediction Based on the Fast Multiple Principal Component Extraction and Optimized Broad Learning G. Xu et al. https://doi.org/10.3390/w14213374
- Machine-Learning-Based Prediction Modeling for Debris Flow Occurrence: A Meta-Analysis L. Yang et al. https://doi.org/10.3390/w16070923
- Early post‐fire debris‐flow initiation following the March 2024 Yajiang Fire, Sichuan, China Y. Zhou et al. https://doi.org/10.1002/esp.70380
- Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province J. Xiong et al. https://doi.org/10.3390/ijgi9020133
- A multi-algorithm fusion approach for identifying post-earthquake rainfall-induced debris flow catchments H. Yin et al. https://doi.org/10.1007/s10064-025-04495-6
- Advanced wind speed prediction using convective weather variables through machine learning application B. Md Abul Ehsan et al. https://doi.org/10.1016/j.acags.2019.100002
- Hydrological post-processing using stacked generalization of quantile regression algorithms: Large-scale application over CONUS H. Tyralis et al. https://doi.org/10.1016/j.jhydrol.2019.123957
- Weather-related thresholds for wildfire danger in a Mediterranean region: The case of Greece K. Papagiannaki et al. https://doi.org/10.1016/j.agrformet.2020.108076
- Spatial clustering-based method for Italian marginal areas toward the sustainable regeneration M. Pignatelli et al. https://doi.org/10.48264/VVSIEV-20233207
- Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective T. Jin et al. https://doi.org/10.3390/rs14061306
- Spatiotemporal analysis of weather-related fire danger associated with climate change in the Zagros Mountains, Iran G. Roshan et al. https://doi.org/10.1007/s00477-024-02850-9
- Susceptibility assessment of “2020.3.30” Xichang post-fire debris flow using a machine learning method T. Jin et al. https://doi.org/10.1088/1755-1315/861/6/062039
- Evaluating methods for debris-flow prediction based on rainfall in an Alpine catchment J. Hirschberg et al. https://doi.org/10.5194/nhess-21-2773-2021
- A hybrid SVR-PSO model to predict concentration of sediment in typical and debris floods M. Kazemi et al. https://doi.org/10.1007/s12145-021-00570-0
- Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA L. McGuire et al. https://doi.org/10.5194/nhess-24-1357-2024
- Evaluating the thresholds for predicting post-earthquake debris flows: Comparison of meteorological, hydro-meteorological and critical discharge approaches Z. Wei et al. https://doi.org/10.1016/j.enggeo.2024.107773
- Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China Y. Zhang et al. https://doi.org/10.3390/rs11232801
- Применение модели логистической регрессии при принятии решений по определению количества привлекаемых сил на ликвидацию лесных пожаров Д. Медведев et al. https://doi.org/10.22227/0869-7493.2024.33.04.84-96
- Machine learning prediction of post-earthquake debris flows incorporating hydrological variables X. Wang et al. https://doi.org/10.1007/s11069-026-07970-9
- Exploring the Application of a Debris Flow Likelihood Regression Model in Mediterranean Post-Fire Environments, Using Field Observations-Based Validation M. Diakakis et al. https://doi.org/10.3390/land12030555
- Evaluation of a fire safety risk prediction model for an existing building W. Rzaij & B. Al-Obaidi https://doi.org/10.1515/jmbm-2022-0007
- Fire Risk Assessment Models Using Statistical Machine Learning and Optimized Risk Indexing M. Choi & S. Jun https://doi.org/10.3390/app10124199
- Predicting post-wildfire debris flow onset using machine learning models on multi-parameter experimental data M. Movasat & I. Tomac https://doi.org/10.1007/s43503-026-00094-w
- Augmentation of WRF-Hydro to simulate overland-flow- and streamflow-generated debris flow susceptibility in burn scars C. Li et al. https://doi.org/10.5194/nhess-22-2317-2022
- Minimal effect of prescribed burning on fire spread rate and intensity in savanna ecosystems A. Moustakas & O. Davlias https://doi.org/10.1007/s00477-021-01977-3
Saved (final revised paper)
Latest update: 20 Aug 2026
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 on 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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