Articles | Volume 16, issue 1
https://doi.org/10.5194/nhess-16-103-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-16-103-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
18 Jan 2016
Research article |
| 18 Jan 2016
Assessing the performance of regional landslide early warning models: the EDuMaP method
M. Calvello
Department of Civil Engineering, University of Salerno, Fisciano, Italy
Department of Civil Engineering, University of Salerno, Fisciano, Italy
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Cited
38 citations as recorded by crossref.
- Validation of landslide hazard models using a semantic engine on online news A. Battistini et al. 10.1016/j.apgeog.2017.03.003
- Design and implementation of site-specific rainfall-induced landslide early warning and monitoring system: a case study at Nam Dan landslide (Vietnam) Q. Gian et al. 10.1080/19475705.2017.1401561
- The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides I. Krøgli et al. 10.5194/nhess-18-1427-2018
- Monitoring strategies for local landslide early warning systems G. Pecoraro et al. 10.1007/s10346-018-1068-z
- Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting N. Nocentini et al. 10.3389/feart.2023.1152130
- A Regional-Scale Landslide Early Warning System Based on the Sequential Evaluation Method: Development and Performance Analysis J. Park et al. 10.3390/app10175788
- Definition and application of a multi-criteria algorithm to identify landslide acceleration phases A. Valletta et al. 10.1080/17499518.2021.1952610
- Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides L. Piciullo et al. 10.1007/s10346-016-0750-2
- Rainfall thresholds for possible landslide occurrence in Italy S. Peruccacci et al. 10.1016/j.geomorph.2017.03.031
- Territorial early warning systems for rainfall-induced landslides L. Piciullo et al. 10.1016/j.earscirev.2018.02.013
- Keynote lecture. Landslide Early Warning Systems: Resources or Problems? F. Guzzetti et al. 10.1051/e3sconf/202341503010
- Innovative Monitoring Tools and Early Warning Systems for Risk Management: A Case Study A. Segalini et al. 10.3390/geosciences9020062
- A review of the recent literature on rainfall thresholds for landslide occurrence S. Segoni et al. 10.1007/s10346-018-0966-4
- Regional early warning model for rainfall induced landslide based on slope unit in Chongqing, China S. Liu et al. 10.1016/j.enggeo.2024.107464
- Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles H. Moayedi et al. 10.3390/s19214698
- Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) N. Nocentini et al. 10.1007/s10346-024-02287-9
- Dual tree-boosting framework for estimating warning levels of rainfall-induced landslides K. Pham et al. 10.1007/s10346-022-01894-8
- Modelling of shallow landslides with machine learning algorithms Z. Liu et al. 10.1016/j.gsf.2020.04.014
- Construction and preliminary analysis of landslide database triggered by heavy storm in the parallel range-valley area of western Chongqing, China, on 8 June 2017 J. Liu & C. Xu 10.3389/feart.2024.1420425
- Quantitative Monitoring of Geological Hazards With CORS Network Data and Load Impact Change: A Case Study in Zhejiang, China P. Xu et al. 10.1109/ACCESS.2020.3038637
- Prediction of Landslide Displacement Based on the Combined VMD-Stacked LSTM-TAR Model Y. Gao et al. 10.3390/rs14051164
- Assessment of building damage due to excavation-induced displacements: The GIBV method L. Piciullo et al. 10.1016/j.tust.2020.103673
- Landslide prediction, monitoring and early warning: a concise review of state-of-the-art B. Chae et al. 10.1007/s12303-017-0034-4
- Rainfall threshold for landslide activity in Dazhou, southwest China H. Yang et al. 10.1007/s10346-019-01270-z
- Geographical landslide early warning systems F. Guzzetti et al. 10.1016/j.earscirev.2019.102973
- Risk assessment and management of rainfall-induced landslides in tropical regions: a review M. Amarasinghe et al. 10.1007/s11069-023-06277-3
- Standards for the performance assessment of territorial landslide early warning systems L. Piciullo et al. 10.1007/s10346-020-01486-4
- Integrating local pore water pressure monitoring in territorial early warning systems for weather-induced landslides G. Pecoraro & M. Calvello 10.1007/s10346-020-01599-w
- A Systematic Review of Existing Early Warning Systems’ Challenges and Opportunities in Cloud Computing Early Warning Systems I. Agbehadji et al. 10.3390/cli11090188
- A systematic review on rainfall thresholds for landslides occurrence F. Gonzalez et al. 10.1016/j.heliyon.2023.e23247
- Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides R. Greco & L. Pagano 10.5194/nhess-17-2213-2017
- Rainfall Induced Shallow Landslide Temporal Probability Modelling and Early Warning Research in Mountains Areas: A Case Study of Qin-Ba Mountains, Western China Y. Song et al. 10.3390/rs14235952
- Threshold Definition for Monitoring Gapa Landslide under Large Variations in Reservoir Level Using GNSS S. Wu et al. 10.3390/rs13244977
- Adapting the EDuMaP method to test the performance of the Norwegian early warning system for weather-induced landslides L. Piciullo et al. 10.5194/nhess-17-817-2017
- Application of a fuzzy verification framework for the evaluation of a regional-scale landslide early warning system during the January 2020 Gloria storm in Catalonia (NE Spain) R. Palau et al. 10.1007/s10346-022-01854-2
- Deep learning forecast of rainfall-induced shallow landslides A. Mondini et al. 10.1038/s41467-023-38135-y
- Independent demonstration of a deep-learning system for rainfall-induced landslide forecasting in Italy F. Guzzetti et al. 10.1007/s10346-024-02294-w
- Comparative analysis of conventional and machine learning techniques for rainfall threshold evaluation under complex geological conditions N. Dal Seno et al. 10.1007/s10346-024-02336-3
37 citations as recorded by crossref.
- Validation of landslide hazard models using a semantic engine on online news A. Battistini et al. 10.1016/j.apgeog.2017.03.003
- Design and implementation of site-specific rainfall-induced landslide early warning and monitoring system: a case study at Nam Dan landslide (Vietnam) Q. Gian et al. 10.1080/19475705.2017.1401561
- The Norwegian forecasting and warning service for rainfall- and snowmelt-induced landslides I. Krøgli et al. 10.5194/nhess-18-1427-2018
- Monitoring strategies for local landslide early warning systems G. Pecoraro et al. 10.1007/s10346-018-1068-z
- Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting N. Nocentini et al. 10.3389/feart.2023.1152130
- A Regional-Scale Landslide Early Warning System Based on the Sequential Evaluation Method: Development and Performance Analysis J. Park et al. 10.3390/app10175788
- Definition and application of a multi-criteria algorithm to identify landslide acceleration phases A. Valletta et al. 10.1080/17499518.2021.1952610
- Definition and performance of a threshold-based regional early warning model for rainfall-induced landslides L. Piciullo et al. 10.1007/s10346-016-0750-2
- Rainfall thresholds for possible landslide occurrence in Italy S. Peruccacci et al. 10.1016/j.geomorph.2017.03.031
- Territorial early warning systems for rainfall-induced landslides L. Piciullo et al. 10.1016/j.earscirev.2018.02.013
- Keynote lecture. Landslide Early Warning Systems: Resources or Problems? F. Guzzetti et al. 10.1051/e3sconf/202341503010
- Innovative Monitoring Tools and Early Warning Systems for Risk Management: A Case Study A. Segalini et al. 10.3390/geosciences9020062
- A review of the recent literature on rainfall thresholds for landslide occurrence S. Segoni et al. 10.1007/s10346-018-0966-4
- Regional early warning model for rainfall induced landslide based on slope unit in Chongqing, China S. Liu et al. 10.1016/j.enggeo.2024.107464
- Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles H. Moayedi et al. 10.3390/s19214698
- Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) N. Nocentini et al. 10.1007/s10346-024-02287-9
- Dual tree-boosting framework for estimating warning levels of rainfall-induced landslides K. Pham et al. 10.1007/s10346-022-01894-8
- Modelling of shallow landslides with machine learning algorithms Z. Liu et al. 10.1016/j.gsf.2020.04.014
- Construction and preliminary analysis of landslide database triggered by heavy storm in the parallel range-valley area of western Chongqing, China, on 8 June 2017 J. Liu & C. Xu 10.3389/feart.2024.1420425
- Quantitative Monitoring of Geological Hazards With CORS Network Data and Load Impact Change: A Case Study in Zhejiang, China P. Xu et al. 10.1109/ACCESS.2020.3038637
- Prediction of Landslide Displacement Based on the Combined VMD-Stacked LSTM-TAR Model Y. Gao et al. 10.3390/rs14051164
- Assessment of building damage due to excavation-induced displacements: The GIBV method L. Piciullo et al. 10.1016/j.tust.2020.103673
- Landslide prediction, monitoring and early warning: a concise review of state-of-the-art B. Chae et al. 10.1007/s12303-017-0034-4
- Rainfall threshold for landslide activity in Dazhou, southwest China H. Yang et al. 10.1007/s10346-019-01270-z
- Geographical landslide early warning systems F. Guzzetti et al. 10.1016/j.earscirev.2019.102973
- Risk assessment and management of rainfall-induced landslides in tropical regions: a review M. Amarasinghe et al. 10.1007/s11069-023-06277-3
- Standards for the performance assessment of territorial landslide early warning systems L. Piciullo et al. 10.1007/s10346-020-01486-4
- Integrating local pore water pressure monitoring in territorial early warning systems for weather-induced landslides G. Pecoraro & M. Calvello 10.1007/s10346-020-01599-w
- A Systematic Review of Existing Early Warning Systems’ Challenges and Opportunities in Cloud Computing Early Warning Systems I. Agbehadji et al. 10.3390/cli11090188
- A systematic review on rainfall thresholds for landslides occurrence F. Gonzalez et al. 10.1016/j.heliyon.2023.e23247
- Basic features of the predictive tools of early warning systems for water-related natural hazards: examples for shallow landslides R. Greco & L. Pagano 10.5194/nhess-17-2213-2017
- Rainfall Induced Shallow Landslide Temporal Probability Modelling and Early Warning Research in Mountains Areas: A Case Study of Qin-Ba Mountains, Western China Y. Song et al. 10.3390/rs14235952
- Threshold Definition for Monitoring Gapa Landslide under Large Variations in Reservoir Level Using GNSS S. Wu et al. 10.3390/rs13244977
- Adapting the EDuMaP method to test the performance of the Norwegian early warning system for weather-induced landslides L. Piciullo et al. 10.5194/nhess-17-817-2017
- Application of a fuzzy verification framework for the evaluation of a regional-scale landslide early warning system during the January 2020 Gloria storm in Catalonia (NE Spain) R. Palau et al. 10.1007/s10346-022-01854-2
- Deep learning forecast of rainfall-induced shallow landslides A. Mondini et al. 10.1038/s41467-023-38135-y
- Independent demonstration of a deep-learning system for rainfall-induced landslide forecasting in Italy F. Guzzetti et al. 10.1007/s10346-024-02294-w
Saved (final revised paper)
Latest update: 02 Apr 2025
Short summary
An original approach, called EDuMaP method, is proposed to assess the performance of landslide early warning models operating at regional scale. EDuMaP comprises three successive steps: definition and temporal analysis of warning and landslide events (E); computation of a duration matrix (DuMa); evaluation of the warning model performance (P). The EDuMaP method may be easily adapted to evaluate the performance of regional early warning models addressing other hazardous phenomena.
An original approach, called EDuMaP method, is proposed to assess the performance of landslide...
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