Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images
International audience Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and dive...
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Online Access: | https://hal.science/hal-03201750 https://hal.science/hal-03201750/document https://hal.science/hal-03201750/file/2020_Heredia_Journal%20of%20Glaciology.pdf https://doi.org/10.1017/jog.2020.11 |
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ftunivnantes:oai:HAL:hal-03201750v1 2023-05-15T16:57:28+02:00 Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel Erosion torrentielle neige et avalanches (UR ETGR (ETNA)) Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Mathematics and computing applied to oceanic and atmospheric flows (AIRSEA) Inria Grenoble - Rhône-Alpes Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Grenoble Alpes (UGA)-Laboratoire Jean Kuntzmann (LJK) Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) Labex OSUG@2020 ANR-15-IDEX-0002,UGA,IDEX UGA(2015) 2020 https://hal.science/hal-03201750 https://hal.science/hal-03201750/document https://hal.science/hal-03201750/file/2020_Heredia_Journal%20of%20Glaciology.pdf https://doi.org/10.1017/jog.2020.11 en eng HAL CCSD International Glaciological Society info:eu-repo/semantics/altIdentifier/doi/10.1017/jog.2020.11 hal-03201750 https://hal.science/hal-03201750 https://hal.science/hal-03201750/document https://hal.science/hal-03201750/file/2020_Heredia_Journal%20of%20Glaciology.pdf doi:10.1017/jog.2020.11 WOS: 000531857800003 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 0022-1430 EISSN: 1727-5652 Journal of Glaciology https://hal.science/hal-03201750 Journal of Glaciology, 2020, 66 (257), pp.373-385. ⟨10.1017/jog.2020.11⟩ Avalanches glaciological instruments and methods glaciological natural hazards [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [SDE]Environmental Sciences info:eu-repo/semantics/article Journal articles 2020 ftunivnantes https://doi.org/10.1017/jog.2020.11 2023-03-08T03:06:20Z International audience Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are now increasingly available from test-sites, but for measurements made along flow propagation, potential autocorrelation should be explicitly accounted for. To this aim, this work proposes a comprehensive Bayesian calibration and statistical model selection framework. As a proof of concept, the framework was applied to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret test-site and a synthetic data set based on the avalanche are used to test the approach and to illustrate its benefits. Results demonstrate (1) the efficiency of the proposed calibration scheme, and (2) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. Our approach could be extended without loss of generality to the calibration of any avalanche dynamics model from any type of measurement stemming from the same avalanche flow. Article in Journal/Newspaper Journal of Glaciology Université de Nantes: HAL-UNIV-NANTES Journal of Glaciology 66 257 373 385 |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
op_collection_id |
ftunivnantes |
language |
English |
topic |
Avalanches glaciological instruments and methods glaciological natural hazards [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [SDE]Environmental Sciences |
spellingShingle |
Avalanches glaciological instruments and methods glaciological natural hazards [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [SDE]Environmental Sciences Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
topic_facet |
Avalanches glaciological instruments and methods glaciological natural hazards [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology [SDE]Environmental Sciences |
description |
International audience Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are now increasingly available from test-sites, but for measurements made along flow propagation, potential autocorrelation should be explicitly accounted for. To this aim, this work proposes a comprehensive Bayesian calibration and statistical model selection framework. As a proof of concept, the framework was applied to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret test-site and a synthetic data set based on the avalanche are used to test the approach and to illustrate its benefits. Results demonstrate (1) the efficiency of the proposed calibration scheme, and (2) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. Our approach could be extended without loss of generality to the calibration of any avalanche dynamics model from any type of measurement stemming from the same avalanche flow. |
author2 |
Erosion torrentielle neige et avalanches (UR ETGR (ETNA)) Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) Mathematics and computing applied to oceanic and atmospheric flows (AIRSEA) Inria Grenoble - Rhône-Alpes Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Grenoble Alpes (UGA)-Laboratoire Jean Kuntzmann (LJK) Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) Labex OSUG@2020 ANR-15-IDEX-0002,UGA,IDEX UGA(2015) |
format |
Article in Journal/Newspaper |
author |
Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel |
author_facet |
Heredia, María Belén Eckert, Nicolas Prieur, Clémentine Thibert, Emmanuel |
author_sort |
Heredia, María Belén |
title |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_short |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_full |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_fullStr |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_full_unstemmed |
Bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
title_sort |
bayesian calibration of an avalanche model from autocorrelated measurements along the flow: application to velocities extracted from photogrammetric images |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.science/hal-03201750 https://hal.science/hal-03201750/document https://hal.science/hal-03201750/file/2020_Heredia_Journal%20of%20Glaciology.pdf https://doi.org/10.1017/jog.2020.11 |
genre |
Journal of Glaciology |
genre_facet |
Journal of Glaciology |
op_source |
ISSN: 0022-1430 EISSN: 1727-5652 Journal of Glaciology https://hal.science/hal-03201750 Journal of Glaciology, 2020, 66 (257), pp.373-385. ⟨10.1017/jog.2020.11⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1017/jog.2020.11 hal-03201750 https://hal.science/hal-03201750 https://hal.science/hal-03201750/document https://hal.science/hal-03201750/file/2020_Heredia_Journal%20of%20Glaciology.pdf doi:10.1017/jog.2020.11 WOS: 000531857800003 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1017/jog.2020.11 |
container_title |
Journal of Glaciology |
container_volume |
66 |
container_issue |
257 |
container_start_page |
373 |
op_container_end_page |
385 |
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1766049017122258944 |