Landslide failure forecast in near-real-time

We present a new method to achieve failure forecast of landslide phenomena by considering near-real-time monitoring data. Starting from the inverse velocity theory, we jointly analyse landslide surface displacements on different time windows, and apply straightforward statistical methods to obtain c...

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Main Authors: Andrea Manconi, Daniele Giordan
Format: Article
Language:English
Published: Taylor & Francis Group 2016-03-01
Series:Geomatics, Natural Hazards & Risk
Online Access:http://dx.doi.org/10.1080/19475705.2014.942388
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spelling doaj-f080020192ed436cb8c2d316898015912020-11-24T22:10:37ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132016-03-017263964810.1080/19475705.2014.942388942388Landslide failure forecast in near-real-timeAndrea Manconi0Daniele Giordan1CNR IRPI, Strada delle Cacce 73CNR IRPI, Strada delle Cacce 73We present a new method to achieve failure forecast of landslide phenomena by considering near-real-time monitoring data. Starting from the inverse velocity theory, we jointly analyse landslide surface displacements on different time windows, and apply straightforward statistical methods to obtain confidence intervals on the forecasted time of failure. Our results can be relevant to support the management of early warning systems during landslide emergency conditions, also when the predefined displacement and/or velocity thresholds are exceeded. In addition, our statistical approach for the definition of confidence interval and forecast reliability can be applied also to different failure forecast methods. We applied for the first time the herein presented approach in near-real-time during the emergency scenario relevant to the reactivation of the La Saxe rockslide, a large mass wasting menacing the population of Courmayeur, northern Italy, and the important European route E25. Our results show how the application of simplified but robust forecast models can be a convenient method to manage and support early warning systems during critical situations.http://dx.doi.org/10.1080/19475705.2014.942388
collection DOAJ
language English
format Article
sources DOAJ
author Andrea Manconi
Daniele Giordan
spellingShingle Andrea Manconi
Daniele Giordan
Landslide failure forecast in near-real-time
Geomatics, Natural Hazards & Risk
author_facet Andrea Manconi
Daniele Giordan
author_sort Andrea Manconi
title Landslide failure forecast in near-real-time
title_short Landslide failure forecast in near-real-time
title_full Landslide failure forecast in near-real-time
title_fullStr Landslide failure forecast in near-real-time
title_full_unstemmed Landslide failure forecast in near-real-time
title_sort landslide failure forecast in near-real-time
publisher Taylor & Francis Group
series Geomatics, Natural Hazards & Risk
issn 1947-5705
1947-5713
publishDate 2016-03-01
description We present a new method to achieve failure forecast of landslide phenomena by considering near-real-time monitoring data. Starting from the inverse velocity theory, we jointly analyse landslide surface displacements on different time windows, and apply straightforward statistical methods to obtain confidence intervals on the forecasted time of failure. Our results can be relevant to support the management of early warning systems during landslide emergency conditions, also when the predefined displacement and/or velocity thresholds are exceeded. In addition, our statistical approach for the definition of confidence interval and forecast reliability can be applied also to different failure forecast methods. We applied for the first time the herein presented approach in near-real-time during the emergency scenario relevant to the reactivation of the La Saxe rockslide, a large mass wasting menacing the population of Courmayeur, northern Italy, and the important European route E25. Our results show how the application of simplified but robust forecast models can be a convenient method to manage and support early warning systems during critical situations.
url http://dx.doi.org/10.1080/19475705.2014.942388
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