Article
Environmental Sciences
Hao Deng, Shengfang Zhou, Yong He, Zeduo Lan, Yanhong Zou, Xiancheng Mao
Summary: Numerical modeling is crucial for understanding the dynamics of contaminants transport in groundwater. This paper presents a Bayesian optimization method that efficiently calibrates numerical models of groundwater contaminant transport. The method utilizes a probabilistic surrogate model and an expected improvement acquisition function to improve the efficiency of model calibration.
Article
Environmental Sciences
Quanzhou Li, Yun Pan, Chong Zhang, Huili Gong
Summary: This study quantifies the uncertainties of groundwater storage (GWS) estimates in mainland China using GRACE satellites. It utilizes multiple data sources and applies the Bayesian model averaging approach to derive optimal estimates of GWS changes. The results show that the annual GWS trend in mainland China is -1.93 mm/yr with an uncertainty of 4.50 mm/yr, highlighting the importance of considering multi-source uncertainties when using GRACE data.
Article
Environmental Sciences
Maryam Gharekhani, Ata Allah Nadiri, Rahman Khatibi, Sina Sadeghfam, Asghar Asghari Moghaddam
Summary: Bayesian Model Averaging (BMA) was used in this study to assess groundwater vulnerability in a study area related to Lake Urmia. The results showed higher uncertainties in areas with higher vulnerability.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Engineering, Civil
Jina Yin, Frank T-C Tsai, Shih-Chieh Kao
Summary: Alluvial aquifers are complex in nature and developing a reliable groundwater model for them is challenging. This study presents a Bayesian multi-model uncertainty quantification framework to account for model parameter uncertainty in alluvial groundwater modeling, improving our understanding of groundwater dynamics and prediction reliability. The methodology was applied to the agriculturally intensive Mississippi River alluvial aquifer in Northeast Louisiana, demonstrating the importance of explicitly quantifying model uncertainty in improving groundwater level predictions.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Jina Yin, Josue Medellin-Azuara, Alvar Escriva-Bou, Zhu Liu
Summary: This study introduces a novel machine learning-based groundwater ensemble modeling framework combined with Bayesian model averaging to predict groundwater storage change in agricultural regions with improved reliability.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Environmental Sciences
Pedram Darbandsari, Paulin Coulibaly
Summary: This study evaluates the impact of different hydrologic models on the performance of the hydrologic uncertainty processor (HUP) and proposes a multimodel Bayesian postprocessor (HUP-BMA). Results demonstrate the superiority of HUP-BMA in quantifying hydrologic uncertainty and forecasting compared to traditional HUP and BMA methods.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Ting Zhou, Xiaohu Wen, Qi Feng, Haijiao Yu, Haiyang Xi
Summary: In this study, the ability of various data sources and machine learning models in predicting groundwater levels was investigated. The results showed that the applied data inputs and the ensemble BMA model significantly improved the predictive accuracy of the single models.
Article
Computer Science, Information Systems
Yong Yu, Xiaosheng Si, Changhua Hu, Jianfei Zheng, Jianxun Zhang
Summary: The study utilizes the Bayesian-updated ECM algorithm and modified Bayesian-model-averaging method to effectively address the uncertainties of model parameters and the degradation model in online RUL estimation. Simulation studies demonstrate that the proposed fusion algorithm significantly improves the prediction of gyroscope RUL.
SCIENCE CHINA-INFORMATION SCIENCES
(2021)
Article
Engineering, Civil
Xue Jiang, Rui Ma, Yanxin Wang, Wenlong Gu, Wenxi Lu, Jin Na
Summary: This study proposes a new two-stage surrogate-assisted Markov chain Monte Carlo-based Bayesian framework for identifying contaminant source parameters in groundwater. An adaptive update feedback process and a multiobjective feasibility-enhanced particle swarm optimization algorithm are utilized to enhance the accuracy and efficiency of the framework.
JOURNAL OF HYDROLOGY
(2021)
Article
Mathematics, Interdisciplinary Applications
Qingzhi Hou, Chunfu Miao, Shaokang Chen, Zewei Sun, Alireza Karemat
Summary: In this paper, a parallelized Lagrangian particle model is proposed for simulating contaminant transport in groundwater. By using smoothed particle hydrodynamics (SPH) method and corrective smoothed particle method (CSPM), the inherent particle inconsistency problem of traditional methods is solved. The numerical results are in good agreement with measured data.
COMPUTATIONAL PARTICLE MECHANICS
(2023)
Article
Engineering, Civil
Sharvil Alex Faroz, Siddhartha Ghosh
Summary: This paper introduces a method to estimate the corrosion rate of reinforced concrete structures through instrument calibration using probabilistic measurement error models within a Bayesian framework and hyper-robust calibration approach. The proposed approach is demonstrated for a linear polarisation resistance instrument and found to be suitable for the study case, as well as general enough to be applied to other NDT instruments.
Article
Engineering, Mechanical
Wanxin He, Gang Li, Zhaokun Nie
Summary: A sparse PDD metamodel based on Bayesian LASSO and adaptive candidate basis selection and model updating method is proposed in this study, which can improve computational accuracy when design samples are limited.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Meteorology & Atmospheric Sciences
Jiawen Xu, Xiaotong Zhang, Weiyu Zhang, Ning Hou, Chunjie Feng, Shuyue Yang, Kun Jia, Yunjun Yao, Xianhong Xie, Bo Jiang, Jie Cheng, Xiang Zhao, Shunlin Liang
Summary: Surface downward longwave radiation (SDLR) plays an important role in understanding the greenhouse effect and global warming. This study evaluated the simulated SDLR from 47 coupled models in the Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) and compared them with ground measurements and CMIP5 results. The study found that the precision of the SDLR simulations varied at different sites and altitudes in the CMIP6 GCMs. Additionally, the Bayesian model averaging (BMA) method improved the correlation and accuracy of the SDLR predictions compared to individual CMIP6 GCMs.
ATMOSPHERIC RESEARCH
(2022)
Article
Economics
Carlos Aller, Lorenzo Ductor, Daryna Grechyna
Summary: The study identifies GDP per capita, the share of fossil fuels in energy consumption, urbanization, industrialization, democratization, the indirect effects of trade, and political polarization as the robust determinants of CO2 emissions per capita. These determinants all negatively impact the environment except political polarization. Additionally, the determinants of CO2 emissions are found to vary depending on a country's level of income per capita.
Article
Environmental Sciences
Young Hoon Song, Eun-Sung Chung, Shamsuddin Shahid
Summary: This study compares the performance of LSTM networks and SWAT in simulating observed runoff and projecting future runoff. The results show that LSTM has better capability in reproducing observed runoff and estimating future runoff.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)