Use of Stacked Gaussian Processes Regression Method to Improve Prediction of Groundwater Solute Transport Model

[1]  M. Boucher,et al.  Comparing numerical modelling, traditional machine learning and theory-guided machine learning in inverse modeling of groundwater dynamics: a first study case application , 2022, Journal of Hydrology.

[2]  Matthew J. Knowling,et al.  Sequential and batch data assimilation approaches to cope with groundwater model error: An empirical evaluation , 2022, Environ. Model. Softw..

[3]  Dong Wang,et al.  Evaluation of Gaussian process regression kernel functions for improving groundwater prediction , 2021, Journal of Hydrology.

[4]  Yuanyuan Zha,et al.  A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation , 2019, Advances in Water Resources.

[5]  Zhifang Zhou,et al.  Experiment and Numerical Simulation of Seawater Intrusion under the Influences of Tidal Fluctuation and Groundwater Exploitation in Coastal Multilayered Aquifers , 2019, Geofluids.

[6]  Dirk Mallants,et al.  Hydrogeological conceptual model building and testing: A review , 2019, Journal of Hydrology.

[7]  D. Roubinet,et al.  Stochastic inversion for soil hydraulic parameters in the presence of model error: An example involving ground-penetrating radar monitoring of infiltration , 2019, Journal of Hydrology.

[8]  Gabriel Terejanu,et al.  Geospatial uncertainty modeling using Stacked Gaussian Processes , 2018, Environ. Model. Softw..

[9]  K. Sudheer,et al.  Enhancement of Model Reliability by Integrating Prediction Interval Optimization into Hydrogeological Modeling , 2018, Water Resources Management.

[10]  J. Man,et al.  Inverse Modeling of Hydrologic Systems with Adaptive Multifidelity Markov Chain Monte Carlo Simulations , 2017, Water Resources Research.

[11]  E. Auken,et al.  Probabilistic predictions using a groundwater model informed with airborne EM data , 2017 .

[12]  Feng Liang,et al.  Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data‐driven error model , 2017 .

[13]  Dongbin Xiu,et al.  Numerical strategy for model correction using physical constraints , 2016, J. Comput. Phys..

[14]  Hui Li,et al.  A stacked gaussian process for predicting geographical incidence of aflatoxin with quantified uncertainties , 2015, SIGSPATIAL/GIS.

[15]  Albert J. Valocchi,et al.  A Bayesian approach to improved calibration and prediction of groundwater models with structural error , 2015 .

[16]  Jenný Brynjarsdóttir,et al.  Learning about physical parameters: the importance of model discrepancy , 2014 .

[17]  Jasper A. Vrugt,et al.  High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing , 2012 .

[18]  Patrick M. Reed,et al.  Save now, pay later? Multi-period many-objective groundwater monitoring design given systematic model errors and uncertainty , 2011 .

[19]  Barbara S. Minsker,et al.  Integrating a calibrated groundwater flow model with error-correcting data-driven models to improve predictions. , 2009 .

[20]  David Bruce Lewis.  Response of wetland soil carbon to groundwater conservation: Probabilistic outcomes from error propagation , 2016 .

[21]  D. Higdon,et al.  Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling , 2009 .

[22]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .