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Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles
Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles
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Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-Ensembles

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Water resources management, 2022, Vol.36 (1), p.39-54 [Periódico revisado por pares]

Dordrecht: Springer Netherlands

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2
Deep learning shows declining groundwater levels in Germany until 2100 due to climate change
Deep learning shows declining groundwater levels in Germany until 2100 due to climate change
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Deep learning shows declining groundwater levels in Germany until 2100 due to climate change

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Nature communications, 2022-03, Vol.13 (1), p.1221-1221, Article 1221 [Periódico revisado por pares]

England: Nature Publishing Group

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3
Comparison of GRACE data and groundwater levels for the assessment of groundwater depletion in Jordan
Comparison of GRACE data and groundwater levels for the assessment of groundwater depletion in Jordan
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Comparison of GRACE data and groundwater levels for the assessment of groundwater depletion in Jordan

Liesch, Tanja ; Ohmer, Marc

Hydrogeology journal, 2016-09, Vol.24 (6), p.1547-1563 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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4
Karst spring discharge modeling based on deep learning using spatially distributed input data
Karst spring discharge modeling based on deep learning using spatially distributed input data
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Karst spring discharge modeling based on deep learning using spatially distributed input data

Wunsch, Andreas ; Liesch, Tanja ; Cinkus, Guillaume ; Ravbar, NataÅ¡a ; Chen, Zhao ; Mazzilli, Naomi ; Jourde, Hervé ; Goldscheider, Nico

Hydrology and earth system sciences, 2022-05, Vol.26 (9), p.2405-2430 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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5
Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)
Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)
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Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX)

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Journal of hydrology (Amsterdam), 2018-12, Vol.567, p.743-758 [Periódico revisado por pares]

Elsevier B.V

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6
Aquifer responses to long-term climatic periodicities
Aquifer responses to long-term climatic periodicities
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Aquifer responses to long-term climatic periodicities

Liesch, Tanja ; Wunsch, Andreas

Journal of hydrology (Amsterdam), 2019-05, Vol.572, p.226-242 [Periódico revisado por pares]

Elsevier B.V

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7
Hydrogeological site investigation and economic evaluation to assess the potential of managed aquifer recharge in the Lower Jordan Valley
Hydrogeological site investigation and economic evaluation to assess the potential of managed aquifer recharge in the Lower Jordan Valley
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Hydrogeological site investigation and economic evaluation to assess the potential of managed aquifer recharge in the Lower Jordan Valley

Xanke, Julian ; Salman, Amer ; Al-Karablieh, Emad ; Liesch, Tanja ; Salameh, Elias ; Goldscheider, Nico

Hydrogeology journal, 2020-03, Vol.28 (2), p.745-762 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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8
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)
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Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

Hydrology and earth system sciences, 2021-04, Vol.25 (3), p.1671-1687 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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9
Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices
Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices
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Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices

Karimanzira, Divas ; Weis, Jonas ; Wunsch, Andreas ; Ritzau, Linda ; Liesch, Tanja ; Ohmer, Marc

Frontiers in water, 2023-07, Vol.5 [Periódico revisado por pares]

Frontiers Media S.A

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10
Uranium in groundwater — Fertilizers versus geogenic sources
Uranium in groundwater — Fertilizers versus geogenic sources
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Uranium in groundwater — Fertilizers versus geogenic sources

Liesch, Tanja ; Hinrichsen, Sören ; Goldscheider, Nico

The Science of the total environment, 2015-12, Vol.536, p.981-995 [Periódico revisado por pares]

Netherlands: Elsevier B.V

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