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A cloud-based framework for sensitivity analysis of natural hazard models


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Ujjwal, KC, Garg, S ORCID: 0000-0003-3510-2464, Hilton, J and Aryal, J ORCID: 0000-0002-4875-2127 2020 , 'A cloud-based framework for sensitivity analysis of natural hazard models' , Environmental Modelling & Software, vol. 134 , pp. 1-14 , doi: 10.1016/j.envsoft.2020.104800.

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Computational models for natural hazards usually require a large number of input parameters that affect the model outcome in a complex manner. The sensitivity of the input parameters to the output variables can be quantified using sensitivity analysis, which provides insight into the key factors driving the model and can guide modeling optimization. However, performing a sensitivity analysis typically requires a large number of simulations, which can be prohibitively time-consuming on workstations or local servers. To address this issue, this study proposes a Cloud-based framework that takes advantage of scalable Cloud resources. The efficacy of the framework is demonstrated by the scalability achieved while running large-scale wildfire simulations. Moreover, a comprehensive sensitivity analysis of the input parameters used in these models is presented. The ability to efficiently perform sensitivity analysis using the framework could allow such analysis to be performed as an on-demand service for operational disaster management.

Item Type: Article
Authors/Creators:Ujjwal, KC and Garg, S and Hilton, J and Aryal, J
Keywords: parameter uncertainty, uncertainty quantification, wildfire modeling, cloud computing, spark, sensitivity analysis
Journal or Publication Title: Environmental Modelling & Software
Publisher: Elsevier Sci Ltd
ISSN: 1364-8152
DOI / ID Number: 10.1016/j.envsoft.2020.104800
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© 2020 Elsevier Ltd

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