Statistical analysis with missing data
Statistical analysis with missing data
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models
A deterministic air quality forecasting system for Torino urban area, Italy
Environmental Modelling & Software
Uncertainty decomposition in environmental modelling and mapping
Proceedings of the 2007 Summer Computer Simulation Conference
The EM algorithm in a distributed computing environment for modelling environmental space-time data
Environmental Modelling & Software
The EM algorithm in a distributed computing environment for modelling environmental space-time data
Environmental Modelling & Software
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In this paper, hierarchical models are proposed as a general approach for spatio-temporal problems, including dynamical mapping, and the analysis of the outputs from complex environmental modeling chains. In this frame, it is easy to define various model components concerning both model outputs and empirical data and to cover with both spatial and temporal correlation. Moreover, special sensitivity analysis techniques are developed for understanding both model components and mapping capability. The motivating application is the dynamical mapping of airborne particulate matters for risk monitoring using data from both a monitoring network and a computer model chain, which includes an emission, a meteorological and a chemical-transport module. Model estimation is determined by the Expectation-Maximization (EM) algorithm associated with simulation-based spatio-temporal parametric bootstrap. Applying sensitivity analysis techniques to the same hierarchical model provides interesting insights into the computer model chain.