Követés
Xiaoqing Shi(施小清)
Xiaoqing Shi(施小清)
Professor of Hydrogeology, Nanjing University
E-mail megerősítve itt: nju.edu.cn
Cím
Hivatkozott rá
Hivatkozott rá
Év
Deep convolutional encoder‐decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneous media
S Mo, Y Zhu, N Zabaras, X Shi, J Wu
Water Resources Research 55 (1), 703-728, 2019
2632019
Deep autoregressive neural networks for high‐dimensional inverse problems in groundwater contaminant source identification
S Mo, N Zabaras, X Shi, J Wu
Water Resources Research 55 (5), 3856-3881, 2019
1962019
Transport, retention, and size perturbation of graphene oxide in saturated porous media: effects of input concentration and grain size
Y Sun, B Gao, SA Bradford, L Wu, H Chen, X Shi, J Wu
Water research 68, 24-33, 2015
1882015
Removal of levofloxacin from aqueous solution using rice-husk and wood-chip biochars
S Yi, B Gao, Y Sun, J Wu, X Shi, B Wu, X Hu
Chemosphere 150, 694-701, 2016
1372016
Regional land subsidence simulation in Su-xi-Chang area and Shanghai City, China
X Shi, J Wu, S Ye, Y Zhang, Y Xue, Z Wei, Q Li, J Yu
Engineering Geology 100 (1-2), 27-42, 2008
1182008
The effects of artificial recharge of groundwater on controlling land subsidence and its influence on groundwater quality and aquifer energy storage in Shanghai, China
X Shi, S Jiang, H Xu, F Jiang, Z He, J Wu
Environmental Earth Sciences 75, 1-18, 2016
972016
Characterization of land subsidence induced by groundwater withdrawals in Su-Xi-Chang area, China
XQ Shi, YQ Xue, SJ Ye, JC Wu, Y Zhang, J Yu
Environmental geology 52 (1), 27-40, 2007
972007
Sustainable development and utilization of groundwater resources considering land subsidence in Suzhou, China
X Shi, R Fang, J Wu, H Xu, YY Sun, J Yu
Engineering geology 124, 77-89, 2012
962012
Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non‐Gaussian hydraulic conductivities
S Mo, N Zabaras, X Shi, J Wu
Water Resources Research 56 (2), e2019WR026082, 2020
882020
Characterization of regional land subsidence in Yangtze Delta, China: the example of Su-Xi-Chang area and the city of Shanghai
X Shi, Y Xue, J Wu, S Ye, Y Zhang, Z Wei, J Yu
Hydrogeology Journal 16, 593-607, 2008
852008
Assessment of parametric uncertainty for groundwater reactive transport modeling
X Shi, M Ye, GP Curtis, GL Miller, PD Meyer, M Kohler, S Yabusaki, J Wu
Water Resources Research 50 (5), 4416-4439, 2014
712014
The development and control of the land subsidence in the Yangtze Delta, China
J Wu, X Shi, Y Xue, Y Zhang, Z Wei, J Yu
Environmental Geology 55, 1725-1735, 2008
612008
Retention and transport of graphene oxide in water-saturated limestone media
S Dong, Y Sun, B Gao, X Shi*, H Xu, J Wu, J Wu*
Chemosphere 180, 506–512, 2017
592017
Effects of grain size and structural heterogeneity on the transport and retention of nano-TiO2 in saturated porous media
X Lv, B Gao, Y Sun, S Dong, J Wu, B Jiang, X Shi
Science of the total environment 563, 987-995, 2016
582016
Numerical simulation of land subsidence induced by groundwater overexploitation in Su-Xi-Chang area, China
JC Wu, XQ Shi, SJ Ye, YQ Xue, Y Zhang, J Yu
Environmental geology 57, 1409-1421, 2009
562009
Numerical simulation of viscoelastoplastic land subsidence due to groundwater overdrafting in Shanghai, China
J Wu, X Shi, S Ye, Y Xue, Y Zhang, Z Wei, Z Fang
Journal of Hydrologic Engineering 15 (3), 223-236, 2010
532010
Influence of flow velocity and spatial heterogeneity on DNAPL migration in porous media: Insights from laboratory experiments and numerical modelling
F Zheng, Y Gao, Y Sun, X Shi, H Xu, J Wu
Hydrogeology Journal 23 (8), 1703, 2015
522015
Effects of humic acid and solution chemistry on the retention and transport of cerium dioxide nanoparticles in saturated porous media
X Lv, B Gao, Y Sun, X Shi, H Xu, J Wu
Water, Air, & Soil Pollution 225, 1-9, 2014
512014
A Taylor expansion‐based adaptive design strategy for global surrogate modeling with applications in groundwater modeling
S Mo, D Lu, X Shi, G Zhang, M Ye, J Wu, J Wu
Water Resources Research 53 (12), 10802-10823, 2017
502017
Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap
S Mo, Y Zhong, E Forootan, N Mehrnegar, X Yin, J Wu, W Feng, X Shi
Journal of Hydrology 604, 127244, 2022
492022
A rendszer jelenleg nem tudja elvégezni a műveletet. Próbálkozzon újra később.
Cikkek 1–20