Multivariate sensitivity analysis for a large-scale climate impact and adaptation model

01/24/2022
by   Oluwole Oyebamiji, et al.
0

We develop a new efficient methodology for Bayesian global sensitivity analysis for large-scale multivariate data. The focus is on computationally demanding models with correlated variables. A multivariate Gaussian process is used as a surrogate model to replace the expensive computer model. To improve the computational efficiency and performance of the model, compactly supported correlation functions are used. The goal is to generate sparse matrices, which give crucial advantages when dealing with large datasets, where we use cross-validation to determine the optimal degree of sparsity. This method was combined with a robust adaptive Metropolis algorithm coupled with a parallel implementation to speed up the convergence to the target distribution. The method was applied to a multivariate dataset from the IMPRESSIONS Integrated Assessment Platform (IAP2), an extension of the CLIMSAVE IAP, which has been widely applied in climate change impact, adaptation and vulnerability assessments. Our empirical results on synthetic and IAP2 data show that the proposed methods are efficient and accurate for global sensitivity analysis of complex models.

READ FULL TEXT

page 5

page 14

page 29

research
06/24/2020

Global Sensitivity and Domain-Selective Testing for Functional-Valued Responses: An Application to Climate Economy Models

Complex computational models are increasingly used by business and gover...
research
02/18/2023

Emulation methods and adaptive sampling increase the efficiency of sensitivity analysis for computationally expensive models

Models with high-dimensional parameter spaces are common in many applica...
research
01/25/2022

Spatial meshing for general Bayesian multivariate models

Quantifying spatial and/or temporal associations in multivariate geoloca...
research
12/29/2021

Correlation-based sparse inverse Cholesky factorization for fast Gaussian-process inference

Gaussian processes are widely used as priors for unknown functions in st...
research
07/14/2023

Global sensitivity analysis in the limited data setting with application to char combustion

In uncertainty quantification, variance-based global sensitivity analysi...
research
01/15/2021

Sensitivity Prewarping for Local Surrogate Modeling

In the continual effort to improve product quality and decrease operatio...
research
01/16/2018

Functional ANOVA with Multiple Distributions: Implications for the Sensitivity Analysis of Computer Experiments

The functional ANOVA expansion of a multivariate mapping plays a fundame...

Please sign up or login with your details

Forgot password? Click here to reset