Diagnostic Evaluation and Uncertainty Quantification of Earth and Environmental Systems Models

Shuo Wang,Zoe Li,Xander Wang

doi:10.1155/2018/5308012

Abstract

Diagnostic Evaluation and Uncertainty Quantification of Earth and Environmental Systems Models

Highlights

E aim of this special issue is to gather contributions on both theory and application of diagnostic analysis and uncertainty quantification of the Environmental Systems (EES) models
Earth and Environmental Systems (EES) models play a crucial role in facilitating the research and development in science and engineering disciplines
To better conceptualize the EES by using mathematical equations, models are becoming more complex with increasingly more sources of uncertainty, including the characterization of initial and boundary conditions, the errors in observation data, the difficulty in obtaining accurate parameter estimates, and model structural deficiencies. erefore, it is necessary to address complexities and uncertainties propagated through the EES models in order to advance the understanding of nonlinear dynamics and chaos in the EES and to enhance the reliability of model results

Summary

Introduction

Editorial Diagnostic Evaluation and Uncertainty Quantification of Earth and Environmental Systems Models E aim of this special issue is to gather contributions on both theory and application of diagnostic analysis and uncertainty quantification of the EES models. E paper entitled “Planning of Electric Power Systems Considering Virtual Power Plants with Dispatchable Loads Included: An Inexact Two-Stage Stochastic Linear Programming Model” by C. Zhou et al proposes an inexact two-stage stochastic linear programming method for supporting sustainable management of electric power systems under uncertainty.

Results

Conclusion