Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design-Reference-Cited by-同舟云学术

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

Published:2023-08 Issue: Volume:178 Page:104489
ISSN:0309-1708
Container-title:Advances in Water Resources
language:en
Short-container-title:Advances in Water Resources

Author:

Tseng Chien-Yung,Ghadiri Maryam,Kumar Praveen,Meidani Hadi

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference36 articles.

1. A review of surrogate models and their application to groundwater modeling;Asher;Water Resour. Res.,2015

2. Lognormal ordinary kriging metamodel in simulation optimization;Balaban;Int. J. (ORAJ),2018

3. Bayesian experimental design: a review;Chaloner;Statist. Sci.,1995

4. Fernández-Godino, M.G., C. Park, N.H. Kim, and R.T. Haftka. 2016. "Review of multi-fidelity models." arXiv preprint (arXiv:1609.07196).

5. Multi-fidelity optimization via surrogate modelling;Forrester;Mathemat., Phys. Engin. Sci.,2007

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1. Assessment of hydraulic conductivity of compacted clayey soil using artificial neural network: an investigation on structural and database multicollinearity;Earth Science Informatics;2024-06-05

2. Deep-Learning-Enhanced CT Image Analysis for Predicting Hydraulic Conductivity of Coarse-Grained Soils;Water;2023-07-19

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