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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)
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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2
Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning
Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning
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Towards understanding the influence of seasons on low-groundwater periods based on explainable machine learning

Wunsch, Andreas ; Liesch, Tanja ; Goldscheider, Nico

Hydrology and earth system sciences, 2024-05, Vol.28 (9), p.2167-2178 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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3
On the challenges of global entity-aware deep learning models for groundwater level prediction
On the challenges of global entity-aware deep learning models for groundwater level prediction
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On the challenges of global entity-aware deep learning models for groundwater level prediction

Heudorfer, Benedikt ; Liesch, Tanja ; Broda, Stefan

Hydrology and earth system sciences, 2024-02, Vol.28 (3), p.525-543 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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4
Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods
Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods
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Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods

Ohmer, Marc ; Liesch, Tanja ; Wunsch, Andreas

Hydrology and earth system sciences, 2022-08, Vol.26 (15), p.4033-4053 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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5
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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6
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
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When best is the enemy of good – critical evaluation of performance criteria in hydrological models

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

Hydrology and earth system sciences, 2023-07, Vol.27 (13), p.2397-2411 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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7
Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions
Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions
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Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions

Cinkus, Guillaume ; Wunsch, Andreas ; Mazzilli, Naomi ; Liesch, Tanja ; Chen, Zhao ; Ravbar, NataÅ¡a ; Doummar, Joanna ; Fernández-Ortega, Jaime ; Barberá, Juan Antonio ; Andreo, Bartolomé ; Goldscheider, Nico ; Jourde, Hervé

Hydrology and earth system sciences, 2023-05, Vol.27 (10), p.1961-1985 [Periódico revisado por pares]

Katlenburg-Lindau: Copernicus GmbH

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8
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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9
Pine Chip and Poultry Litter Derived Biochars Affect C and N Dynamics in Two Georgia, USA, Ultisols
Pine Chip and Poultry Litter Derived Biochars Affect C and N Dynamics in Two Georgia, USA, Ultisols
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Pine Chip and Poultry Litter Derived Biochars Affect C and N Dynamics in Two Georgia, USA, Ultisols

Weyers, Sharon L. ; Das, Keshav C. ; Gaskin, Julia W. ; Liesch, Amanda M.

Agronomy (Basel), 2023-02, Vol.13 (2), p.531 [Periódico revisado por pares]

Basel: MDPI AG

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10
A bacterial toxin-antitoxin module is the origin of inter-bacterial and inter-kingdom effectors of Bartonella
A bacterial toxin-antitoxin module is the origin of inter-bacterial and inter-kingdom effectors of Bartonella
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A bacterial toxin-antitoxin module is the origin of inter-bacterial and inter-kingdom effectors of Bartonella

Harms, Alexander ; Liesch, Marius ; Körner, Jonas ; Québatte, Maxime ; Engel, Philipp ; Dehio, Christoph Viollier, Patrick H.

PLoS genetics, 2017-10, Vol.13 (10), p.e1007077-e1007077 [Periódico revisado por pares]

United States: Public Library of Science

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