Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4603-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-4603-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2019
Research article |
| 15 Nov 2019
| 15 Nov 2019
Modelling of the shallow water table at high spatial resolution using random forests
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), Copenhagen, 1350, Denmark
Helen Berger
COWI A/S, Lyngby, 2800, Denmark
Hans Jørgen Henriksen
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), Copenhagen, 1350, Denmark
Torben Obel Sonnenborg
Department of Hydrology, Geological Survey of Denmark and Greenland
(GEUS), Copenhagen, 1350, Denmark
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51 citations as recorded by crossref.
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- A combined hydrodynamic model and deep learning method to predict water level in ungauged rivers G. Li et al. 10.1016/j.jhydrol.2023.130025
- Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts M. Ali et al. 10.1016/j.jhydrol.2020.124647
- Causes and implications of groundwater depletion in India: A review S. Dangar et al. 10.1016/j.jhydrol.2021.126103
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- Impacts of land use, climate change and hydrological model structure on nitrate fluxes: Magnitudes and uncertainties I. Seidenfaden et al. 10.1016/j.scitotenv.2022.154671
- Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest S. He et al. 10.1016/j.chemosphere.2021.133388
- A data-driven method for the estimation of shallow landslide runout A. Giarola et al. 10.1016/j.catena.2023.107573
- Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California V. Agarwal et al. 10.1016/j.scitotenv.2022.161138
- Reconstructing high-resolution groundwater level data using a hybrid random forest model to quantify distributed groundwater changes in the Indus Basin A. Arshad et al. 10.1016/j.jhydrol.2023.130535
- Groundwater level dynamics in a subtropical fan delta region and its future prediction using machine learning tools: Sustainable groundwater restoration S. Mahammad et al. 10.1016/j.ejrh.2023.101385
- Response mechanism of groundwater dynamics to Freeze–thaw process in seasonally frozen soil areas: A comprehensive analysis from site to regional scale H. Lyu et al. 10.1016/j.jhydrol.2023.129861
- The potential of data driven approaches for quantifying hydrological extremes S. Hauswirth et al. 10.1016/j.advwatres.2021.104017
- Regional Agroclimate Characteristic and Its Multiple Teleconnections: A Case Study in the Jianghan Plain (JHP) Region W. Li et al. 10.3390/w13192789
- Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth R. Schneider et al. 10.5194/hess-26-5859-2022
- Effect and genesis of soil nitrogen loading and hydrogeological conditions on the distribution of shallow groundwater nitrogen pollution in the North China Plain Q. Xia et al. 10.1016/j.watres.2023.120346
- Upscaling approach to help determine variability in riparian lowland flow dynamics S. Noorduijn et al. 10.1016/j.jhydrol.2023.129536
- Mapping the groundwater memory across Ireland: A step towards a groundwater drought susceptibility assessment P. Schuler et al. 10.1016/j.jhydrol.2022.128277
- Spatial prediction of groundwater levels using machine learning and geostatistical models: a case study of coastal faulted aquifer systems in southeastern Tunisia H. Chihi & I. Ben Cheikh Larbi 10.1007/s10040-023-02686-7
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- Exploring the spatiotemporal water quality variations and their influencing factors in a large floodplain lake in China B. Li et al. 10.1016/j.ecolind.2020.106454
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- Evaluation of shallow groundwater dynamics after water supplement in North China Plain based on attention-GRU model T. Nan et al. 10.1016/j.jhydrol.2023.130085
- Groundwater level estimation in northern region of Bangladesh using hybrid locally weighted linear regression and Gaussian process regression modeling A. Elbeltagi et al. 10.1007/s00704-022-04037-0
- Effective groundwater level recovery from mining reduction: Case study of Baoding and Shijiazhuang Plain area T. Nan et al. 10.26599/JGSE.2023.9280023
- A New Digital Twin for Climate Change Adaptation, Water Management, and Disaster Risk Reduction (HIP Digital Twin) H. Henriksen et al. 10.3390/w15010025
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- High Resolution Water Table Modeling of the Shallow Groundwater Using a Knowledge-Guided Gradient Boosting Decision Tree Model J. Koch et al. 10.3389/frwa.2021.701726
- Prediction of Groundwater Level Variations in a Changing Climate: A Danish Case Study R. Gonzalez & J. Arsanjani 10.3390/ijgi10110792
- Groundwater level forecasting with machine learning models: A review K. Boo et al. 10.1016/j.watres.2024.121249
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- A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations R. Schneider et al. 10.1016/j.jhydrol.2022.128339
- Hydrological process knowledge in catchment modelling – Lessons and perspectives from 60 years development J. Refsgaard et al. 10.1002/hyp.14463
- The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting S. Hauswirth et al. 10.5194/hess-27-501-2023
- Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation M. Sattari et al. 10.5194/hess-25-603-2021
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- Machine learning for predicting shallow groundwater levels in urban areas A. LaBianca et al. 10.1016/j.jhydrol.2024.130902
- A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk M. Rohde et al. 10.3389/feart.2021.784499
- Downscaling a national hydrological model to subgrid scale S. Noorduijn et al. 10.1016/j.jhydrol.2021.126796
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- Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems G. Kopsiaftis et al. 10.3389/frwa.2023.1195029
- Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns J. Bikše et al. 10.1016/j.jhydrol.2023.129424
- Estimation of groundwater age distributions from hydrochemistry: comparison of two metamodelling algorithms in the Heretaunga Plains aquifer system, New Zealand C. Tschritter et al. 10.5194/hess-27-4295-2023
Latest update: 18 Apr 2024
Short summary
This study explores novel modelling avenues using machine learning in combination with process-based models to predict the shallow water table at high spatial resolution. Due to climate change and anthropogenic impacts, the shallow groundwater is rising in many parts of the world. In order to adapt to risks induced by groundwater flooding, new modelling tools need to emerge. In this study, we found that machine learning is capable of reaching the required accuracy and resolution.
This study explores novel modelling avenues using machine learning in combination with...
