Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2021WR030436 |
A Two-stage Bayesian Data-driven Method to Improve Model Prediction | |
Xiaozhuo Sun; Xiankui Zeng; Jichun Wu; Dong Wang | |
2021-12-03 | |
发表期刊 | Water Resources Research
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出版年 | 2021 |
英文摘要 | Because of the earth system complexity, groundwater/hydrology models are always built with structural errors, which may lead to systematic errors in model predictions. Bayesian data-driven methods (DDMs) provide a feasible way to correct systematic model errors statistically. Generally, the physical and statistical model parameters, namely physical parameters and hyperparameters, are assumed to be independent and jointly calibrated. However, this assumption may be unreasonable and lead to over-adjusted parameter estimation and biased model prediction. This study proposes a two-stage DDM to calibrate physical parameters and hyperparameters separately, which does not make the independence assumption. Three case studies, including a groundwater solute transport analytical model, a three-dimensional groundwater flow model, and a real-world snowmelt runoff model, were used to evaluate the predictive performance of this two-stage DDM. Based on the three case studies, we found that the independence assumption of physical parameters and hyperparameters could lead to the over-fitting of parameter estimation and deviations in model predictions. Two-stage DDM can constrain the systematic error model calibration; that is, physical parameters are first calibrated in the entire hyperparameter prior probability space, and then hyperparameters are calibrated in the posterior probability space of physical parameters obtained previously. As a result, compared with traditional joint calibration-based DDM, two-stage DDM can alleviate parameter over-fitting and improve model predictive performance. This article is protected by copyright. All rights reserved. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/343050 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Xiaozhuo Sun,Xiankui Zeng,Jichun Wu,et al. A Two-stage Bayesian Data-driven Method to Improve Model Prediction[J]. Water Resources Research,2021. |
APA | Xiaozhuo Sun,Xiankui Zeng,Jichun Wu,&Dong Wang.(2021).A Two-stage Bayesian Data-driven Method to Improve Model Prediction.Water Resources Research. |
MLA | Xiaozhuo Sun,et al."A Two-stage Bayesian Data-driven Method to Improve Model Prediction".Water Resources Research (2021). |
条目包含的文件 | 条目无相关文件。 |
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