Articles | Volume 16, issue 12
https://doi.org/10.5194/nhess-16-2501-2016
© 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-16-2501-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Landslide forecasting and factors influencing predictability
Emanuele Intrieri
CORRESPONDING AUTHOR
Department of Earth Sciences, University of Studies of Firenze, via
La Pira 4, 50121 Florence, Italy
Giovanni Gigli
Department of Earth Sciences, University of Studies of Firenze, via
La Pira 4, 50121 Florence, Italy
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Cited
44 citations as recorded by crossref.
- A comparative study of different kinematics - based methods to predict the time of slope failure S. Philip et al. 10.1088/1757-899X/1116/1/012158
- Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method C. Zhou et al. 10.1007/s10346-018-1022-0
- On the monitoring and early-warning of brittle slope failures in hard rock masses: Examples from an open-pit mine T. Carlà et al. 10.1016/j.enggeo.2017.08.007
- Relationship between river bank stability and hydrological processes using in situ measurement data G. Mentes 10.1556/24.62.2019.01
- A reliability evaluation of four landslide failure forecasting methods in real-time monitoring applications S. Sharifi et al. 10.1007/s10346-024-02293-x
- Deep learning powered long-term warning systems for reservoir landslides T. Zeng et al. 10.1016/j.ijdrr.2023.103820
- Correlation between Acoustic Emission Behaviour and Dynamics Model during Three-Stage Deformation Process of Soil Landslide L. Deng et al. 10.3390/s21072373
- Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al. 10.1038/s43017-022-00373-x
- Landslide-prone areas in Makale Selatan with the analytical hierarchy process method Y. Sarma et al. 10.1088/1755-1315/1230/1/012083
- Can satellite InSAR innovate the way of large landslide early warning? P. Zeng et al. 10.1016/j.enggeo.2024.107771
- A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards J. Chen et al. 10.1080/13658816.2023.2273877
- Algorithms to enhance detection of landslide acceleration moment and time-to-failure forecast using time-series displacements S. Sharifi et al. 10.1016/j.enggeo.2022.106832
- Landslide displacement prediction by using Bayesian optimization–temporal convolutional networks J. Yang et al. 10.1007/s11440-023-02205-8
- Analyzing the formation mechanism of the Xuyong landslide, Sichuan province, China, and emergency monitoring based on multiple remote sensing platform techniques Y. Luo et al. 10.1080/19475705.2020.1745903
- Pinpointing Early Signs of Impending Slope Failures From Space S. Zhou et al. 10.1029/2021JB022957
- Reply to discussion on “Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses” by F. Bozzano, P. Mazzanti, and S. Moretto T. Carlà et al. 10.1007/s10346-018-0991-3
- A new prediction method for the occurrence of landslides based on the time history of tilting of the slope surface J. Xie et al. 10.1007/s10346-019-01283-8
- Forecasting the time of failure of landslides at slope-scale: A literature review E. Intrieri et al. 10.1016/j.earscirev.2019.03.019
- Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques C. Luu et al. 10.1016/j.asr.2024.08.046
- Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China Q. Ling et al. 10.1007/s11069-021-04713-w
- Satellite Interferometry as a Tool for Early Warning and Aiding Decision Making in an Open-Pit Mine E. Intrieri et al. 10.1109/JSTARS.2019.2953339
- Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data S. Das et al. 10.1007/s00477-024-02713-3
- When hazard avoidance is not an option: lessons learned from monitoring the postdisaster Oso landslide, USA M. Reid et al. 10.1007/s10346-021-01686-6
- KLC2020 implementation: challenges for the development of satellite landslide early warning systems E. Intrieri et al. 10.1007/s10346-021-01721-6
- Formation mechanism and dynamic process of open-pit coal mine landslides: a case study of the Xinjing landslide in Inner Mongolia, China Q. Wang et al. 10.1007/s10346-023-02193-6
- Automatic approach for increasing the location accuracy of slow-moving landslide endogenous seismicity: the APOLoc method F. Provost et al. 10.1093/gji/ggy330
- Experimental Study on the Relationship between the Velocity of Surface Movements and Tilting Rate in Pre-Failure Stage of Rainfall-Induced Landslides J. Xie et al. 10.3390/s21185988
- An approach for prospective forecasting of rock slope failure time J. Leinauer et al. 10.1038/s43247-023-00909-z
- Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations Z. Ma & G. Mei 10.1016/j.jrmge.2024.02.034
- A spatial case-based reasoning method for regional landslide risk assessment Z. Zhao et al. 10.1016/j.jag.2021.102381
- Landslide time-of-failure forecast and alert threshold assessment: A generalized criterion A. Segalini et al. 10.1016/j.enggeo.2018.08.003
- Bayesian machine learning-based method for prediction of slope failure time J. Zhang et al. 10.1016/j.jrmge.2021.09.010
- A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models Z. Zhao & J. Chen 10.1080/17538947.2023.2174192
- Kinematics and Controlling Factors of Slow-Moving Landslides in Central Texas: A Multisource Data Fusion Approach E. Gebremichael et al. 10.3390/geosciences14050133
- Indeterminacy of displacement and stress of geologic rock mass system in the critically non-stationary state: implication on prediction of geo-hazards Z. Feng et al. 10.1007/s11069-021-04611-1
- Lateral edifice collapse and volcanic debris avalanches: a post-1980 Mount St. Helens perspective L. Siebert & M. Reid 10.1007/s00445-023-01662-z
- Alert threshold assessment based on equivalent displacements for the identification of potentially critical landslide events A. Valletta et al. 10.1007/s11069-022-05606-2
- A Stochastic Dynamical Model of Slope Creep and Failure Q. Lei & D. Sornette 10.1029/2022GL102587
- Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems K. Lehnertz 10.1063/5.0214733
- Integration of ground-based radar and satellite InSAR data for the analysis of an unexpected slope failure in an open-pit mine T. Carlà et al. 10.1016/j.enggeo.2018.01.021
- Probabilistic prediction of slope failure time J. Zhang et al. 10.1016/j.enggeo.2020.105586
- Critical assessment of landslide failure forecasting methods with case histories: a comparative study of INV, MINV, SLO, and VOA S. Sharifi et al. 10.1007/s10346-024-02237-5
- Landslide failure time prediction with a new model: case studies in Fushun west open pit mine, China J. Hu et al. 10.1007/s10064-024-03902-8
- The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data E. Intrieri et al. 10.1007/s10346-017-0915-7
43 citations as recorded by crossref.
- A comparative study of different kinematics - based methods to predict the time of slope failure S. Philip et al. 10.1088/1757-899X/1116/1/012158
- Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method C. Zhou et al. 10.1007/s10346-018-1022-0
- On the monitoring and early-warning of brittle slope failures in hard rock masses: Examples from an open-pit mine T. Carlà et al. 10.1016/j.enggeo.2017.08.007
- Relationship between river bank stability and hydrological processes using in situ measurement data G. Mentes 10.1556/24.62.2019.01
- A reliability evaluation of four landslide failure forecasting methods in real-time monitoring applications S. Sharifi et al. 10.1007/s10346-024-02293-x
- Deep learning powered long-term warning systems for reservoir landslides T. Zeng et al. 10.1016/j.ijdrr.2023.103820
- Correlation between Acoustic Emission Behaviour and Dynamics Model during Three-Stage Deformation Process of Soil Landslide L. Deng et al. 10.3390/s21072373
- Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al. 10.1038/s43017-022-00373-x
- Landslide-prone areas in Makale Selatan with the analytical hierarchy process method Y. Sarma et al. 10.1088/1755-1315/1230/1/012083
- Can satellite InSAR innovate the way of large landslide early warning? P. Zeng et al. 10.1016/j.enggeo.2024.107771
- A cellular automaton integrating spatial case-based reasoning for predicting local landslide hazards J. Chen et al. 10.1080/13658816.2023.2273877
- Algorithms to enhance detection of landslide acceleration moment and time-to-failure forecast using time-series displacements S. Sharifi et al. 10.1016/j.enggeo.2022.106832
- Landslide displacement prediction by using Bayesian optimization–temporal convolutional networks J. Yang et al. 10.1007/s11440-023-02205-8
- Analyzing the formation mechanism of the Xuyong landslide, Sichuan province, China, and emergency monitoring based on multiple remote sensing platform techniques Y. Luo et al. 10.1080/19475705.2020.1745903
- Pinpointing Early Signs of Impending Slope Failures From Space S. Zhou et al. 10.1029/2021JB022957
- Reply to discussion on “Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses” by F. Bozzano, P. Mazzanti, and S. Moretto T. Carlà et al. 10.1007/s10346-018-0991-3
- A new prediction method for the occurrence of landslides based on the time history of tilting of the slope surface J. Xie et al. 10.1007/s10346-019-01283-8
- Forecasting the time of failure of landslides at slope-scale: A literature review E. Intrieri et al. 10.1016/j.earscirev.2019.03.019
- Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques C. Luu et al. 10.1016/j.asr.2024.08.046
- Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China Q. Ling et al. 10.1007/s11069-021-04713-w
- Satellite Interferometry as a Tool for Early Warning and Aiding Decision Making in an Open-Pit Mine E. Intrieri et al. 10.1109/JSTARS.2019.2953339
- Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data S. Das et al. 10.1007/s00477-024-02713-3
- When hazard avoidance is not an option: lessons learned from monitoring the postdisaster Oso landslide, USA M. Reid et al. 10.1007/s10346-021-01686-6
- KLC2020 implementation: challenges for the development of satellite landslide early warning systems E. Intrieri et al. 10.1007/s10346-021-01721-6
- Formation mechanism and dynamic process of open-pit coal mine landslides: a case study of the Xinjing landslide in Inner Mongolia, China Q. Wang et al. 10.1007/s10346-023-02193-6
- Automatic approach for increasing the location accuracy of slow-moving landslide endogenous seismicity: the APOLoc method F. Provost et al. 10.1093/gji/ggy330
- Experimental Study on the Relationship between the Velocity of Surface Movements and Tilting Rate in Pre-Failure Stage of Rainfall-Induced Landslides J. Xie et al. 10.3390/s21185988
- An approach for prospective forecasting of rock slope failure time J. Leinauer et al. 10.1038/s43247-023-00909-z
- Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations Z. Ma & G. Mei 10.1016/j.jrmge.2024.02.034
- A spatial case-based reasoning method for regional landslide risk assessment Z. Zhao et al. 10.1016/j.jag.2021.102381
- Landslide time-of-failure forecast and alert threshold assessment: A generalized criterion A. Segalini et al. 10.1016/j.enggeo.2018.08.003
- Bayesian machine learning-based method for prediction of slope failure time J. Zhang et al. 10.1016/j.jrmge.2021.09.010
- A robust discretization method of factor screening for landslide susceptibility mapping using convolution neural network, random forest, and logistic regression models Z. Zhao & J. Chen 10.1080/17538947.2023.2174192
- Kinematics and Controlling Factors of Slow-Moving Landslides in Central Texas: A Multisource Data Fusion Approach E. Gebremichael et al. 10.3390/geosciences14050133
- Indeterminacy of displacement and stress of geologic rock mass system in the critically non-stationary state: implication on prediction of geo-hazards Z. Feng et al. 10.1007/s11069-021-04611-1
- Lateral edifice collapse and volcanic debris avalanches: a post-1980 Mount St. Helens perspective L. Siebert & M. Reid 10.1007/s00445-023-01662-z
- Alert threshold assessment based on equivalent displacements for the identification of potentially critical landslide events A. Valletta et al. 10.1007/s11069-022-05606-2
- A Stochastic Dynamical Model of Slope Creep and Failure Q. Lei & D. Sornette 10.1029/2022GL102587
- Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems K. Lehnertz 10.1063/5.0214733
- Integration of ground-based radar and satellite InSAR data for the analysis of an unexpected slope failure in an open-pit mine T. Carlà et al. 10.1016/j.enggeo.2018.01.021
- Probabilistic prediction of slope failure time J. Zhang et al. 10.1016/j.enggeo.2020.105586
- Critical assessment of landslide failure forecasting methods with case histories: a comparative study of INV, MINV, SLO, and VOA S. Sharifi et al. 10.1007/s10346-024-02237-5
- Landslide failure time prediction with a new model: case studies in Fushun west open pit mine, China J. Hu et al. 10.1007/s10064-024-03902-8
1 citations as recorded by crossref.
Latest update: 05 Nov 2024
Short summary
Forecasting a landslide collapse is a key element in risk reduction, but it is also a very difficult task due to scientific difficulties in predicting a complex natural event and the severe social repercussions caused by a false or missed alarm.
In order to help decision makers, we propose a method of increasing the confidence when making landslide predictions. This study also helps understand how geology affects landslide predictability by introducing a predictability index.
Forecasting a landslide collapse is a key element in risk reduction, but it is also a very...
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