New insights into the temporal prediction of landslides by a terrestrial SAR interferometry monitoring case study

Mazzanti, Paolo; Bozzano, Francesca; Cipriani, Ivan; Prestininzi, Alberto

doi:10.1007/s10346-014-0469-x

Cited by 69 publications

(43 citation statements)

References 33 publications

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“…By contrast, Sp is more difficult to assess, mainly due to the too short historical records of instrumental monitored landslides available for that kind of statistical analysis, (Crosta and Agliardi, 30 2003;Mazzanti et al, 2015).…”

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confidence: 99%

A decision support system (DSS) for critical landslides and rockfalls and its application to some cases in the Western Italian Alps

Bertolo

2017

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Operative geologists who are involved in emergency management have often to deal with the consequences of assuming critical and strongly impacting decisions in uncertain conditions. 10Geohazards induced by active landslides are one of the civil protection situations requiring such decisions.Nowadays, the monitoring of active landslides is almost always supported by numerical early warning systems, based on instrumental geotechnical and topographic networks. These networks provide numerical early warning thresholds, which are set up in order to activate alert conditions at various 15 levels of criticality in an objective way.Despite these progresses the issue related to the possibility to dispatch false alerts has not yet effectively solved and that's the reason why the critical stages of the decisional processes are frequently relying not only on quantitative thresholds but also on the subjective experience of the emergency managers. Therefore it is not so uncommon to read landslide-monitoring procedures that combine the quantitative 20 information provided by the monitoring systems with the qualitative decisional elements coming from their professional experience in order to assume the most correct decision. It's therefore evident that such an approach weakens the objectiveness provided by instrumental monitoring systems but, at the same time, collecting geological empirical and qualitative data can strengthen an hypothesis like the one that an active landslide could finally collapse. 25Bayesian methods are frequently used in clinical decision making, another field of the human activity where critical decisions have to be made in a short time, combining objective values such as those provided by medical tests with diagnostic qualitative markers.Based on the methods of clinical diagnosis, the has author has elaborated a reliable and objective Bayesian Decision Support System (or DSS), developed to support the decision makers in assuming the 30 most correct decisions based on all the elements, both quantitative and qualitative, that are available at a certain step of the decision process.Thanks to the Bayesian approach, the DSS allows also to assess the predictivity of any single decisional step, which is the probability that a monitored landslide actually collapses when particular diagnostic evidences are detected, either instrumental or observational. 35Hence the decision makers who are able to issue a civil protection alert when a given degree of confidence about the chance that a monitored landslide will collapse is reached. The degree of Nat. Hazards Earth Syst. Sci. Discuss., https://doi

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A decision support system (DSS) for critical landslides and rockfalls and its application to some cases in the Western Italian Alps

Bertolo

2017

Preprint

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“…The forecasting methods have great potential for landslide hazard management [16,[18][19][20]. By applying FFMs to actual monitoring data, we have obtained a prediction error lower than 3 days in 53.7% of the examples using the INV method, 51.7% using the LOG technique, 48.2% using the NL technique, and 27.8% using the LSM technique.…”

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confidence: 82%

Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

et al. 2017

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Abstract:In this work, the ability of advanced satellite interferometry to monitor pre-failure landslide behaviours and the potential application of this technique to Failure Forecasting Methods (FFMs) are analysed. Several limits affect the ability of the technique to monitor a landslide process, especially during the pre-failure phase (tertiary creep). In this study, two of the major limitations affecting the technique have been explored: (1) the low data sampling frequency and (2) the phase ambiguity constraints. We explored the time series of displacements for 56 monitored landslides inferred from the scientific literature and from different in situ and remote monitoring instruments (i.e., extensometers, inclinometers, distometers, Ground Base InSAR, and total station). Furthermore, four different forecasting techniques have been applied to the monitoring data of the selected landslides. To analyse the reliability of the FFMs based on the InSAR satellite data, the 56 time series have been sampled based on different satellite features, simulating the satellite revisit time and the phase ambiguity constraints. Our analysis shows that the satellite InSAR technique could be successful in monitoring the landslide's tertiary creep phase and, in some cases, for forecasting the corresponding time of failure using FFMs. However, the low data sampling frequency of the present satellite systems do not capture the necessary detail for the application of FFMs in actual risk management problems or for early warning purposes.

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“…Since landslide risk management generally requires tools that can forecast slope behavior, many authors usually address monitoring systems and focus on their capability to track landslide evolution (Pieraccini et al, 2002;Intrieri et al, 2013;Mazzanti et al, 2015). However, limited efforts have been made to properly study the spatial scale and accuracy of slope physical models for effective landslide numerical analysis.…”

Section: Discussionmentioning

confidence: 99%

The role of the spatial scale and data accuracy on deep-seated gravitational slope deformation modeling: The Ronco landslide, Italy

et al. 2016

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In recent decades numerical models have been developed and extensively used for landslide hazard and risk assessment. The reliability of the outcomes of these numerical simulations must be evaluated carefully as it mainly depends on the soundness of the physical model of the landslide that in turn often requires the integration of several surface and subsurface surveys in order to achieve a satisfactory spatial resolution. Merging diverse sources of data may be particularly complex for large landslides, because of intrinsic heterogeneity and possible great data uncertainty. In this paper, we assess the spatial scale and data accuracy required for effective numerical landslide modeling. We focus on two particular aspects: the model extent and the accuracy of input datasets. The Ronco landslide, a deep-seated gravitational slope deformation (DSGSD) located in the North of Italy, was used as a test-bed. Geological, geomorphological and geophysical data were combined and, as a result, eight models with different spatial scales and data accuracies were obtained. The models were used to run a back analysis of an event in 2002, during which part of the slope moved after intense rainfalls. The results point to the key role of a proper geomorphological zonation to properly set the model extent. The accuracy level of the input datasets should also be tuned. We suggest applying the approach presented here to other DSGSDs with different geological and geomorphological settings to test the reliability of our findings.

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New insights into the temporal prediction of landslides by a terrestrial SAR interferometry monitoring case study

Cited by 69 publications

References 33 publications

A decision support system (DSS) for critical landslides and rockfalls and its application to some cases in the Western Italian Alps

A decision support system (DSS) for critical landslides and rockfalls and its application to some cases in the Western Italian Alps

Assessment of Landslide Pre-Failure Monitoring and Forecasting Using Satellite SAR Interferometry

The role of the spatial scale and data accuracy on deep-seated gravitational slope deformation modeling: The Ronco landslide, Italy

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