A Framework for Probabilistic Multi-Hazard Assessment of Rain-Triggered Lahars Using Bayesian Belief Networks: Difference between revisions

From Disaster Risk Gateway
No edit summary
No edit summary
 
(One intermediate revision by the same user not shown)
Line 3: Line 3:
|Access=Open
|Access=Open
|Link=https://doi.org/10.3389/feart.2017.00073
|Link=https://doi.org/10.3389/feart.2017.00073
|Organisation(s)/Authors=Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Bologna, Italy; School of Earth Sciences, University of Bristol, UK; Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Roma, Italy; UK Meteorological Office
|Author(s)=Tierz, P., Woodhouse, M. J., Phillips, J. C., Sandri, L., Selva, J., Marzocchi, W., & Odbert, H. M.
|Description=A framework for probabilistic hazard assessment of lahars within a multi-hazard environment developed by [https://doi.org/10.3389/feart.2017.00073 Tierz et al. (2017)] that uses a Bayesian Belief Network model (''Multihaz'') for lahar triggering, coupled with a dynamic physical model for lahar propagation (''LaharFlow''). ''Multihaz'' is used to estimate the probability of lahars of different volumes occurring given information about regional rainfall, scientific knowledge on lahar triggering mechanisms and probabilistic assessment of available pyroclastic material from tephra fallout and pyroclastic density currents. ''LaharFlow'' propagates the uncertainty and probabilities modeled by ''Multihaz'' into hazard footprints of lahars.
|Organisation(s)=Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy; School of Earth Sciences, University of Bristol, Bristol, United Kingdom; Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Roma, Rome, Italy; UK Met Office, Exeter, United Kingdom
|Description=A framework for probabilistic hazard assessment of lahars within a multi-hazard environment developed by [https://doi.org/10.3389/feart.2017.00073 Tierz et al. (2017)] that uses a Bayesian Belief Network model (''Multihaz'') for lahar triggering, coupled with a dynamic physical model for lahar propagation (''LaharFlow''). ''Multihaz'' is used to estimate the probability of occurrence of different volumes of lahars given information about regional rainfall, scientific knowledge on lahar triggering mechanisms and probabilistic assessment of available pyroclastic material from tephra fallout and pyroclastic density currents. ''LaharFlow'' propagates the uncertainty and probabilities modeled by ''Multihaz'' into hazard footprints of lahars.
|Technical Considerations=Modelling framework not directly available.
|Technical Considerations=Modelling framework not directly available.
|Key Words=Lahars; triggering hazards; hazard modelling; Bayesian Belief Network
|Key Words=Lahars; triggering hazards; hazard modelling; Bayesian Belief Network
|Organisation(s)/Authors=Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Bologna, Italy; School of Earth Sciences, University of Bristol, UK; Istituto Nazionale di Geofisica e Vulcanologia (INGV), Sezione di Roma, Italy; UK Meteorological Office
}}
}}

Latest revision as of 11:34, 2 April 2025

Publication Year: 2017

Access: Open

Link: https://doi.org/10.3389/feart.2017.00073

Author(s): Tierz, P., Woodhouse, M. J., Phillips, J. C., Sandri, L., Selva, J., Marzocchi, W., & Odbert, H. M.

Organisation(s)/Authors: Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy; School of Earth Sciences, University of Bristol, Bristol, United Kingdom; Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Roma, Rome, Italy; UK Met Office, Exeter, United Kingdom

Description:

A framework for probabilistic hazard assessment of lahars within a multi-hazard environment developed by Tierz et al. (2017) that uses a Bayesian Belief Network model (Multihaz) for lahar triggering, coupled with a dynamic physical model for lahar propagation (LaharFlow). Multihaz is used to estimate the probability of occurrence of different volumes of lahars given information about regional rainfall, scientific knowledge on lahar triggering mechanisms and probabilistic assessment of available pyroclastic material from tephra fallout and pyroclastic density currents. LaharFlow propagates the uncertainty and probabilities modeled by Multihaz into hazard footprints of lahars.

Technical Considerations:

Modelling framework not directly available.

Key Words:

Lahars; triggering hazards; hazard modelling; Bayesian Belief Network