A cloud-based framework for sensitivity analysis of natural hazard models. (December 2020)

• Record Type: Journal Article
• Authors: KC, Ujjwal; Garg, Saurabh; Hilton, James; Aryal, Jagannath
• Is Part Of: Environmental modelling & software. Volume 134(2020)
• Publication Date: 2020-12
• Subjects: Sensitivity analysis -- Parameter uncertainty -- Uncertainty quantification -- Wildfire modeling -- Cloud computing -- Spark; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104800
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Developed and investigated an efficient Cloud-based sensitivity analysis framework. Implemented various sensitivity analysis methods - Morris, Sobol, and FAST. Demonstrated the efficiency of the analysis of large-scale wildfire simulations.