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

Citation

Ujjwal, KC and Garg, S and Hilton, J and Aryal, J, A cloud-based framework for sensitivity analysis of natural hazard models, Environmental Modelling & Software, 134 Article 104800. ISSN 1364-8152 (2020) [Refereed Article]


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Copyright Statement

2020 Elsevier Ltd

DOI: doi:10.1016/j.envsoft.2020.104800

Abstract

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 Details

Item Type:Refereed Article
Keywords:parameter uncertainty, uncertainty quantification, wildfire modeling, cloud computing, spark, sensitivity analysis
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Cloud computing
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Applied computing
UTAS Author:Ujjwal, KC (Mr Ujjwal)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:142771
Year Published:2020
Web of Science® Times Cited:6
Deposited By:Information and Communication Technology
Deposited On:2021-02-11
Last Modified:2021-04-21
Downloads:3 View Download Statistics

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