Gaussian process machine learning and Kriging for groundwater salinity interpolation-Reference-Cited by-同舟云学术

Gaussian process machine learning and Kriging for groundwater salinity interpolation

Published:2021-10 Issue: Volume:144 Page:105170
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Cui Tao^ORCID,Pagendam Dan,Gilfedder Mat^ORCID

Publisher

Elsevier BV

Subject

Ecological Modeling,Environmental Engineering,Software

Reference32 articles.

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2. Probabilistic categorical groundwater salinity mapping from Airborne electromagnetic data adjacent to California's lost hills and Belridge oil fields;Ball;Water Resour. Res.,2020

3. A comparative study among machine learning and numerical models for simulating groundwater dynamics in the heihe river basin, northwestern China;Chen;Sci. Rep.,2020

4. Emulator-enabled approximate Bayesian computation (ABC) and uncertainty analysis for computationally expensive groundwater models;Cui;J. Hydrol.,2018

5. Large-scale, probabilistic salinity mapping using Airborne Electromagnetics for groundwater management in Zeeland, The Netherlands;Delsman;Environ. Res. Lett.,2018

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