Efficient Bayesian experimental design for contaminant source identification J Zhang, L Zeng, C Chen, D Chen, L Wu Water Resources Research 51 (1), 576-598, 2015 | 128 | 2015 |
An adaptive Gaussian process-based method for efficient Bayesian experimental design in groundwater contaminant source identification problems J Zhang, W Li, L Zeng, L Wu Water Resources Research 52 (8), 5971-5984, 2016 | 123 | 2016 |
A multi-medium chain modeling approach to estimate the cumulative effects of cadmium pollution on human health X Liu, L Zhong, J Meng, F Wang, J Zhang, Y Zhi, L Zeng, X Tang, J Xu Environmental Pollution 239, 308-317, 2018 | 78 | 2018 |
An adaptive Gaussian process-based iterative ensemble smoother for data assimilation L Ju, J Zhang, L Meng, L Wu, L Zeng Advances in Water Resources 115, 125-135, 2018 | 61 | 2018 |
An Iterative Local Updating Ensemble Smoother for Estimation and Uncertainty Assessment of Hydrologic Model Parameters With Multimodal Distributions J Zhang, G Lin, W Li, L Wu, L Zeng Water Resources Research 54 (3), 1716-1733, 2018 | 61 | 2018 |
Inverse modeling of hydrologic systems with adaptive multi-fidelity Markov chain Monte Carlo simulations J Zhang, J Man, G Lin, L Wu, L Zeng Water Resources Research 54 (7), 4867-4886, 2018 | 45 | 2018 |
Improving Simulation Efficiency of MCMC for Inverse Modeling of Hydrologic Systems with a Kalman‐Inspired Proposal Distribution J Zhang, JA Vrugt, X Shi, G Lin, L Wu, L Zeng Water Resources Research 56 (3), e2019WR025474, 2020 | 44 | 2020 |
Surrogate‐Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error J Zhang, Q Zheng, D Chen, L Wu, L Zeng Water Resources Research 56 (1), e2019WR025721, 2020 | 44 | 2020 |
Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization J Zhang, Q Zheng, L Wu, L Zeng Water Resources Research 56 (12), e2020WR027399, 2020 | 34 | 2020 |
Efficient evaluation of small failure probability in high-dimensional groundwater contaminant transport modeling via a two-stage Monte Carlo method J Zhang, W Li, G Lin, L Zeng, L Wu Water Resources Research 53 (3), 1948–1962, 2017 | 30 | 2017 |
Adaptive Multi‐Fidelity Data Assimilation for Nonlinear Subsurface Flow Problems Q Zheng, J Zhang, W Xu, L Wu, L Zeng Water Resources Research 55 (1), 203-217, 2019 | 26 | 2019 |
Quantification of the sorption of organic pollutants to minerals via an improved mathematical model accounting for associations between minerals and soil organic matter J Cheng, Q Ye, Z Lu, J Zhang, L Zeng, SJ Parikh, W Ma, C Tang, J Xu, ... Environmental Pollution 280, 116991, 2021 | 21 | 2021 |
Sequential ensemble-based optimal design for parameter estimation J Man, J Zhang, W Li, L Zeng, L Wu Water Resources Research 52 (10), 7577-7592, 2016 | 19 | 2016 |
Water flux characterization through hydraulic head and temperature data assimilation: Numerical modeling and sandbox experiments L Ju, J Zhang, C Chen, L Wu, L Zeng Journal of Hydrology 558, 104-114, 2018 | 16 | 2018 |
Efficient Bayesian Inverse Modeling of Water Infiltration in Layered Soils H Gao, J Zhang, C Liu, J Man, C Chen, L Wu, L Zeng Vadose Zone Journal 18 (1), 2019 | 13 | 2019 |
ANOVA-based multi-fidelity probabilistic collocation method for uncertainty quantification J Man, J Zhang, L Wu, L Zeng Advances in Water Resources 122, 176-186, 2018 | 11 | 2018 |
Characterization of vapor intrusion sites with a deep learning-based data assimilation method J Man, Y Guo, J Jin, J Zhang, Y Yao, J Zhang Journal of Hazardous Materials 431, 128600, 2022 | 9 | 2022 |
地下水污染源解析的贝叶斯监测设计与参数反演方法 张江江 浙江大学, 2017 | 9 | 2017 |
Parameter regionalization based on machine learning optimizes the estimation of reference evapotranspiration in data deficient area Z Shu, Y Zhou, J Zhang, J Jin, L Wang, N Cui, G Wang, J Zhang, H Wu, ... Science of the Total Environment 844, 157034, 2022 | 8 | 2022 |
Bayesian monitoring design for streambed heat tracing: Numerical simulation and sandbox experiments L Ju, J Zhang, L Wu, L Zeng Groundwater 57 (4), 534-546, 2019 | 5 | 2019 |