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142771 - A cloud-based framework for sensitivity analysis of natural hazard models - preprint.pdf (10.48 MB)

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

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posted on 2023-05-20, 20:53 authored by Ujjwal K C, Saurabh GargSaurabh Garg, Hilton, J, Jagannath Aryal
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.

Funding

CSIRO Data61

History

Publication title

Environmental Modelling & Software

Volume

134

Article number

104800

Number

104800

Pagination

1-14

ISSN

1364-8152

Department/School

School of Information and Communication Technology

Publisher

Elsevier Sci Ltd

Place of publication

The Boulevard, Langford Lane, Kidlington, Oxford, England, Oxon, Ox5 1Gb

Rights statement

© 2020 Elsevier Ltd

Repository Status

  • Open

Socio-economic Objectives

Applied computing

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