Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Keyphrases

• Borehole 100%
• Hydraulic Conductivity 100%
• Gaussian Process Regression 100%
• Multi-source Data 100%
• Bayesian Design 100%
• Accurate Estimation 50%
• Parameter Estimation 50%
• Illinois 50%
• Region of Interest 50%
• Confidence Interval 50%
• Electrical Conductivity 50%
• Prediction Model 50%
• Model Prediction 50%
• Random Field 50%
• Observational Data 50%
• Sampling Location 50%
• Design Futures 50%
• Numerical Framework 50%
• Gaussian Process Model 50%
• Hydraulic Properties 50%
• Water Management System 50%
• Pumping Test 50%
• Multi-source Observations 50%
• Geological Formation 50%
• Hydrogeology 50%
• Aquifer Properties 50%
• Multi-fidelity 50%
• Multi-fidelity Modeling 50%
• Multi-Fidelity Gaussian Process 50%

Engineering

• Gaussians 100%
• Design of Experiments 100%
• Hydraulics 100%
• Source Data 100%
• Subsurface 66%
• Model Fidelity 66%
• Borehole 66%
• Region of Interest 33%
• Parameter Estimate 33%
• Water Management 33%
• Random Field 33%
• Model Prediction 33%
• Future Design 33%
• Single Source 33%
• Confidence Interval 33%
• Pumping Test 33%

Earth and Planetary Sciences

• Experimental Design 100%
• Kriging 100%
• Borehole 100%
• Hydraulic Conductivity 100%
• Illinois 50%
• Water Management 50%
• Confidence Interval 50%
• Hydraulic Property 50%
• Hydrogeology 50%