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Investigation of Multi-fidelity Co-Kriging Model for Hydraulic Conductivity in Sangamon Watershed
  • Maryam Ghadiri,
  • Chien-Yung Tseng,
  • Hadi Meidani
Maryam Ghadiri
Illinois Water Resources Center

Corresponding Author:mghadiri@illinois.edu

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Chien-Yung Tseng
University of Illinois at Urbana Champaign
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Hadi Meidani
University of Illinois at Urbana Champaign
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Sangamon watershed is recognized as one of the most worth noting regions for water and environmental supply planning and management purposes according to its intensively management for soybean and corn production. It is also a representative area with limited geological and hydraulic measurement data, in which sustainable ground water and environmental management is essential. To better understand the hydraulic properties of the entire watershed, a multi-fidelity Gaussian Processes (Kriging) model was applied to predict the hydraulic conductivity of the upper Sangamon watershed, using previous multi-sources of field observation data (Electrical Earth Resistivity and pumping test data). The model also provided a quantification of uncertainty of the predicted values, which helps us to make reliable suggestions for the future design of hydraulic observations. The data fidelity effect to the model was discussed by comparing multi-fidelity and single-high-fidelity Kriging results. The model predicted values suggest that the accuracy of multi-fidelity Kriging depends on the locations and the distribution of both the high- and low-fidelity data. When high-fidelity data points are sparse and far away from the low-fidelity data points, the information provided from the low-fidelity data becomes extremely important, which can greatly enhance the model performance and accuracy. This study has paved the way to a more efficient parameter estimation in under-sampled sites by effectively estimating large-scale parameter maps using small-scale measurements and by applying uncertainty quantification method to a real watershed observation case. It will also draw upon and contribute to advances in Bayesian experimental design, and will optimally result in financial savings.