Workflows for Construction of Spatio-Temporal Probabilistic Maps for Volcanic Hazard Assessment

被引:0
|
作者
Jones-Ivey, Renette [1 ]
Patra, Abani [2 ]
Bursik, Marcus [3 ]
机构
[1] SUNY Buffalo, Inst Computat & Data Sci, Buffalo, NY 14222 USA
[2] Tufts Univ, Data Intens Studies Ctr DISC, Medford, MA 02155 USA
[3] SUNY Buffalo, Ctr Geohazards Studies, Buffalo, NY USA
基金
美国国家科学基金会;
关键词
uncertainty quantification; volcanology; hazard mapping; volcanic hazard assessment; Pegasus Workflow Management System; ash cloud; pyroclastic flow; SCIENCE; UNCERTAINTY; SIMULATION; ERUPTION; HUBZERO; PEGASUS; SYSTEM; MODEL; WIND;
D O I
10.3389/feart.2021.744655
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Probabilistic hazard assessments for studying overland pyroclastic flows or atmospheric ash clouds under short timelines of an evolving crisis, require using the best science available unhampered by complicated and slow manual workflows. Although deterministic mathematical models are available, in most cases, parameters and initial conditions for the equations are usually only known within a prescribed range of uncertainty. For the construction of probabilistic hazard assessments, accurate outputs and propagation of the inherent input uncertainty to quantities of interest are needed to estimate necessary probabilities based on numerous runs of the underlying deterministic model. Characterizing the uncertainty in system states due to parametric and input uncertainty, simultaneously, requires using ensemble based methods to explore the full parameter and input spaces. Complex tasks, such as running thousands of instances of a deterministic model with parameter and input uncertainty require a High Performance Computing infrastructure and skilled personnel that may not be readily available to the policy makers responsible for making informed risk mitigation decisions. For efficiency, programming tasks required for executing ensemble simulations need to run in parallel, leading to twin computational challenges of managing large amounts of data and performing CPU intensive processing. The resulting flow of work requires complex sequences of tasks, interactions, and exchanges of data, hence the automatic management of these workflows are essential. Here we discuss a computer infrastructure, methodology and tools which enable scientists and other members of the volcanology research community to develop workflows for construction of probabilistic hazard maps using remotely accessed computing through a web portal.
引用
收藏
页数:13
相关论文
共 50 条
  • [21] Monitoring changes in spatio-temporal maps of disease
    Rodeiro, Carmen L. Vidal
    Lawson, Andrew B.
    BIOMETRICAL JOURNAL, 2006, 48 (03) : 463 - 480
  • [22] Spatio-temporal Computation with Neural Sensorial Maps
    Ferrandez-Vicente, J. M.
    Delgado, A.
    Mira, J.
    METHODS AND MODELS IN ARTIFICIAL AND NATURAL COMPUTATION, PT I: A HOMAGE TO PROFESSOR MIRA'S SCIENTIFIC LEGACY, 2009, 5601 : 79 - +
  • [23] STAM: A Framework for Spatio-Temporal Affordance Maps
    Riccio, Francesco
    Capobianco, Roberto
    Hanheide, Marc
    Nardi, Daniele
    MODELLING AND SIMULATION FOR AUTONOMOUS SYSTEMS, MESAS 2016, 2016, 9991 : 271 - 280
  • [24] SPATIO-TEMPORAL ANALYSIS OF THE AVALANCHE HAZARD IN THE NORTH OF ITALY
    Nicolis, Orietta
    Assuncao, Renato
    STATISTICA, 2013, 73 (01) : 123 - 138
  • [25] Seismic Hazard Assessment in Mines Using a Marked Spatio-Temporal Point Process Model
    Trifu, C-I
    Shumila, V.
    RASIM6: CONTROLLING SEISMIC RISK, 2005, : 461 - 467
  • [26] Spatio-temporal assessment of vulnerability to drought
    Vinit K. Jain
    R. P. Pandey
    Manoj K. Jain
    Natural Hazards, 2015, 76 : 443 - 469
  • [27] Spatio-temporal assessment of vulnerability to drought
    Jain, Vinit K.
    Pandey, R. P.
    Jain, Manoj K.
    NATURAL HAZARDS, 2015, 76 (01) : 443 - 469
  • [28] Probabilistic spatio-temporal inference for motion event understanding
    Choi, Chang
    Choi, Junho
    Lee, Eunji
    You, Ilsun
    Kim, Pankoo
    NEUROCOMPUTING, 2013, 122 : 24 - 32
  • [29] Efficient probabilistic spatio-temporal video object segmentation
    Ahmed, Rakib
    Karmakar, Gour C.
    Dooley, Laurence S.
    6TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE, PROCEEDINGS, 2007, : 807 - +
  • [30] Layered dynamic probabilistic networks for spatio-temporal modelling
    Bui, Hung H.
    Venkatesh, Svetha
    West, Geoff
    Intelligent Data Analysis, 1999, 3 (05): : 339 - 361