Ming Ye
Zitiert von
Zitiert von
Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas
J Zhang, Y Zhu, X Zhang, M Ye, J Yang
Journal of hydrology 561, 918-929, 2018
Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications
X Song, J Zhang, C Zhan, Y Xuan, M Ye, C Xu
Journal of hydrology 523, 739-757, 2015
Towards a comprehensive assessment of model structural adequacy
HV Gupta, MP Clark, JA Vrugt, G Abramowitz, M Ye
Water Resources Research 48 (8), 2012
On model selection criteria in multimodel analysis
M Ye, PD Meyer, SP Neuman
Water Resources Research 44 (3), 2008
Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff
M Ye, SP Neuman, PD Meyer
Water Resources Research 40 (5), 2004
Spatiotemporal variations of hydrogeochemistry and its controlling factors in the Gandaki River Basin, Central Himalaya Nepal
RR Pant, F Zhang, FU Rehman, G Wang, M Ye, C Zeng, H Tang
Science of the Total Environment 622, 770-782, 2018
A model‐averaging method for assessing groundwater conceptual model uncertainty
M Ye, KF Pohlmann, JB Chapman, GM Pohll, DM Reeves
Groundwater 48 (5), 716-728, 2010
Bayesian analysis of data-worth considering model and parameter uncertainties
SP Neuman, L Xue, M Ye, D Lu
Advances in Water Resources 36, 75-85, 2012
Groundwater sustainability: A review of the interactions between science and policy
AS Elshall, AD Arik, AI El-Kadi, S Pierce, M Ye, KM Burnett, CA Wada, ...
Environmental Research Letters 15 (9), 093004, 2020
Ground-based evaluation of MODIS snow cover product V6 across China: Implications for the selection of NDSI threshold
H Zhang, F Zhang, G Zhang, T Che, W Yan, M Ye, N Ma
Science of the Total Environment 651, 2712-2726, 2019
Estimating daily air temperatures over the Tibetan Plateau by dynamically integrating MODIS LST data
H Zhang, F Zhang, M Ye, T Che, G Zhang
Journal of Geophysical Research: Atmospheres 121 (19), 11,425-11,441, 2016
An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling
G Zhang, D Lu, M Ye, M Gunzburger, C Webster
Water Resources Research 49 (10), 6871-6892, 2013
Snow cover and runoff modelling in a high mountain catchment with scarce data: effects of temperature and precipitation parameters
F Zhang, H Zhang, SC Hagen, M Ye, D Wang, D Gui, C Zeng, L Tian, J Liu
Hydrological processes 29 (1), 52-65, 2015
Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification
D Lu, M Ye, MC Hill
Water resources research 48 (9), 2012
Practical use of computationally frugal model analysis methods
MC Hill, D Kavetski, M Clark, M Ye, M Arabi, D Lu, L Foglia, S Mehl
Groundwater 54 (2), 159-170, 2016
Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff
M Ye, SP Neuman, PD Meyer, K Pohlmann
Water Resources Research 41 (12), 2005
Estimation of effective unsaturated hydraulic conductivity tensor using spatial moments of observed moisture plume
TCJ Yeh, M Ye, R Khaleel
Water resources research 41 (3), 2005
Quantifying model structural error: Efficient B ayesian calibration of a regional groundwater flow model using surrogates and a data‐driven error model
T Xu, AJ Valocchi, M Ye, F Liang
Water Resources Research 53 (5), 4084-4105, 2017
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
A fully coupled numerical modeling for regional unsaturated–saturated water flow
Y Zhu, L Shi, L Lin, J Yang, M Ye
Journal of hydrology 475, 188-203, 2012
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