A GUI platform for uncertainty quantification of complex dynamical models

被引：49
|
作者：
Wang, Chen [1 ,2 ]
Duan, Qingyun [1 ,2 ]
Tong, Charles H. [3 ]
Di, Zhenhua [1 ,2 ]
Gong, Wei [1 ,2 ]
机构：
[1] Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, Joint Ctr Global Change Res, Beijing 100875, Peoples R China
[3] Lawrence Livermore Natl Lab, 7000 East Ave, Livermore, CA 94550 USA
来源：
ENVIRONMENTAL MODELLING & SOFTWARE | 2016年 / 76卷
关键词：
Uncertainty Quantification; Design of experiments; Sensitivity analysis; Surrogate modeling; Parameter optimization; UQ-PyL; GLOBAL SENSITIVITY MEASURES; RAINFALL-RUNOFF MODELS; AUTOMATIC CALIBRATION; OPTIMIZATION; DESIGN;
D O I：
10.1016/j.envsoft.2015.11.004
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Uncertainty quantification (UQ) refers to quantitative characterization and reduction of uncertainties present in computer model simulations. It is widely used in engineering and geophysics fields to assess and predict the likelihood of various outcomes. This paper describes a UQ platform called UQ-PyL (Uncertainty Quantification Python Laboratory), a flexible software platform designed to quantify uncertainty of complex dynamical models. UQ-PyL integrates different kinds of UQ methods, including experimental design, statistical analysis, sensitivity analysis, surrogate modeling and parameter optimization. It is written in Python language and runs on all common operating systems. UQ-PyL has a graphical user interface that allows users to enter commands via pull-down menus. It is equipped with a model driver generator that allows any computer model to be linked with the software. We illustrate the different functions of UQ-PyL by applying it to the uncertainty analysis of the Sacramento Soil Moisture Accounting Model. We will also demonstrate that UQ-PyL can be applied to a wide range of applications. (C) 2015 The Authors. Published by Elsevier Ltd.
引用
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页码：1 / 12
页数：12
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