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Jiangjiang Zhang
Jiangjiang Zhang
Hohai University
Verified email at zju.edu.cn
Title
Cited by
Cited by
Year
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
1232015
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
1192016
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
772018
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
602018
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
602018
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
452018
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
422020
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
412020
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
312020
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
282017
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
262019
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
192021
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
192016
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
152018
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
132019
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
112018
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
92022
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
92017
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
82022
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
52019
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Articles 1–20