Articles | Volume 17, issue 10
https://doi.org/10.5194/nhess-17-1713-2017
© Author(s) 2017. 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-17-1713-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Oct 2017
Research article |
| 06 Oct 2017
Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application
Emanuele Intrieri
CORRESPONDING AUTHOR
Department of Earth Sciences, University of Florence, via La Pira 4, 50121, Florence, Italy
Federica Bardi
Department of Earth Sciences, University of Florence, via La Pira 4, 50121, Florence, Italy
Riccardo Fanti
Department of Earth Sciences, University of Florence, via La Pira 4, 50121, Florence, Italy
Giovanni Gigli
Department of Earth Sciences, University of Florence, via La Pira 4, 50121, Florence, Italy
Francesco Fidolini
Pizzi Terra srl, via di Ripoli 207H, 50126, Florence, Italy
Nicola Casagli
Department of Earth Sciences, University of Florence, via La Pira 4, 50121, Florence, Italy
Sandra Costanzo
Department of Informatics, Modeling, Electronics and System Engineering,
University of Calabria, Ponte Pietro Bucci, Cube 41b, 87036, Arcavacata di Rende (CS), Italy
Antonio Raffo
Department of Informatics, Modeling, Electronics and System Engineering,
University of Calabria, Ponte Pietro Bucci, Cube 41b, 87036, Arcavacata di Rende (CS), Italy
Giuseppe Di Massa
Department of Informatics, Modeling, Electronics and System Engineering,
University of Calabria, Ponte Pietro Bucci, Cube 41b, 87036, Arcavacata di Rende (CS), Italy
Giovanna Capparelli
Department of Informatics, Modeling, Electronics and System Engineering,
University of Calabria, Ponte Pietro Bucci, Cube 41b, 87036, Arcavacata di Rende (CS), Italy
Pasquale Versace
Department of Informatics, Modeling, Electronics and System Engineering,
University of Calabria, Ponte Pietro Bucci, Cube 41b, 87036, Arcavacata di Rende (CS), Italy
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Cited
20 citations as recorded by crossref.
- A Dynamic Management and Integration Framework for Models in Landslide Early Warning System L. Liu et al. 10.3390/ijgi12050198
- Spatiotemporal Evolution of a Landslide: A Transition to Explosive Percolation K. Singh & A. Tordesillas 10.3390/e22010067
- 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
- Learning in an interactive simulation tool against landslide risks: the role of strength and availability of experiential feedback P. Chaturvedi et al. 10.5194/nhess-18-1599-2018
- A hydrology-process based method for correlating debris flow density to rainfall parameters and its application on debris flow prediction K. Long et al. 10.1016/j.jhydrol.2020.125124
- Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al. 10.1038/s43017-022-00373-x
- Formulation of landslide risk scenarios using underground monitoring data and numerical models: conceptual approach, analysis, and evolution of a case study in Southern Italy A. Segalini et al. 10.1007/s10346-019-01137-3
- Augmented Intelligence Forecasting and What-If-Scenario Analytics With Quantified Uncertainty for Big Real-Time Slope Monitoring Data A. Tordesillas et al. 10.1109/TGRS.2024.3382302
- Ground-Based Radar Interferometry for Monitoring the Dynamic Performance of a Multitrack Steel Truss High-Speed Railway Bridge Q. Huang et al. 10.3390/rs12162594
- Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception S. Segoni et al. 10.5194/nhess-18-3179-2018
- Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure A. Tordesillas et al. 10.1038/s41598-021-88836-x
- Big Data for Natural Disasters in an Urban Railroad Neighborhood: A Systematic Review T. Correia et al. 10.3390/smartcities3020012
- Remote Sensing Precursors Analysis for Giant Landslides H. Lan et al. 10.3390/rs14174399
- Optimized Apriori algorithm for deformation response analysis of landslide hazards L. Linwei et al. 10.1016/j.cageo.2022.105261
- Rainfall nowcasting model for early warning systems applied to a case over Central Italy D. De Luca & G. Capparelli 10.1007/s11069-021-05191-w
- An Empirical Approach for Modeling Hysteresis Behavior of Pyroclastic Soils G. Capparelli & G. Spolverino 10.3390/hydrology7010014
- Pinpointing Early Signs of Impending Slope Failures From Space S. Zhou et al. 10.1029/2021JB022957
- Transformative role of big data through enabling capability recognition in construction B. Atuahene et al. 10.1080/01446193.2022.2132523
- Landslide detection by deep learning of non-nadiral and crowdsourced optical images F. Catani 10.1007/s10346-020-01513-4
- Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence X. Zhang et al. 10.1007/s11069-024-06673-3
20 citations as recorded by crossref.
- A Dynamic Management and Integration Framework for Models in Landslide Early Warning System L. Liu et al. 10.3390/ijgi12050198
- Spatiotemporal Evolution of a Landslide: A Transition to Explosive Percolation K. Singh & A. Tordesillas 10.3390/e22010067
- 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
- Learning in an interactive simulation tool against landslide risks: the role of strength and availability of experiential feedback P. Chaturvedi et al. 10.5194/nhess-18-1599-2018
- A hydrology-process based method for correlating debris flow density to rainfall parameters and its application on debris flow prediction K. Long et al. 10.1016/j.jhydrol.2020.125124
- Landslide detection, monitoring and prediction with remote-sensing techniques N. Casagli et al. 10.1038/s43017-022-00373-x
- Formulation of landslide risk scenarios using underground monitoring data and numerical models: conceptual approach, analysis, and evolution of a case study in Southern Italy A. Segalini et al. 10.1007/s10346-019-01137-3
- Augmented Intelligence Forecasting and What-If-Scenario Analytics With Quantified Uncertainty for Big Real-Time Slope Monitoring Data A. Tordesillas et al. 10.1109/TGRS.2024.3382302
- Ground-Based Radar Interferometry for Monitoring the Dynamic Performance of a Multitrack Steel Truss High-Speed Railway Bridge Q. Huang et al. 10.3390/rs12162594
- Preface: Landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception S. Segoni et al. 10.5194/nhess-18-3179-2018
- Spatiotemporal slope stability analytics for failure estimation (SSSAFE): linking radar data to the fundamental dynamics of granular failure A. Tordesillas et al. 10.1038/s41598-021-88836-x
- Big Data for Natural Disasters in an Urban Railroad Neighborhood: A Systematic Review T. Correia et al. 10.3390/smartcities3020012
- Remote Sensing Precursors Analysis for Giant Landslides H. Lan et al. 10.3390/rs14174399
- Optimized Apriori algorithm for deformation response analysis of landslide hazards L. Linwei et al. 10.1016/j.cageo.2022.105261
- Rainfall nowcasting model for early warning systems applied to a case over Central Italy D. De Luca & G. Capparelli 10.1007/s11069-021-05191-w
- An Empirical Approach for Modeling Hysteresis Behavior of Pyroclastic Soils G. Capparelli & G. Spolverino 10.3390/hydrology7010014
- Pinpointing Early Signs of Impending Slope Failures From Space S. Zhou et al. 10.1029/2021JB022957
- Transformative role of big data through enabling capability recognition in construction B. Atuahene et al. 10.1080/01446193.2022.2132523
- Landslide detection by deep learning of non-nadiral and crowdsourced optical images F. Catani 10.1007/s10346-020-01513-4
- Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence X. Zhang et al. 10.1007/s11069-024-06673-3
Discussed (final revised paper)
Latest update: 14 Dec 2024
Short summary
Landslides are a threat not only to people but also to important infrastructure, like highways. Nowadays there are several monitoring systems that are able to detect slope displacements in order to give prompt alarms. On the other hand, such instruments produce a huge amount of information, which is often not totally used and which can also represent an issue for data storage and transmission. In this paper we explain how we dealt with the large quantity of data provided by one of these tools.
Landslides are a threat not only to people but also to important infrastructure, like highways....
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Final-revised paper
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