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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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2
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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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]

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
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory
Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory
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Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory

Wunsch, Andreas ; Liesch, Tanja ; Broda, Stefan

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

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]

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]

Copernicus GmbH

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8
Acknowledgement to MIR Board Members and Ad hoc Reviewers
Acknowledgement to MIR Board Members and Ad hoc Reviewers
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Acknowledgement to MIR Board Members and Ad hoc Reviewers

Oesterle, Michael-Jorg ; Wolf, Joachim

Management international review, 2023-02, Vol.63 (1), p.1-1 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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9
Acknowledgement to MIR Board Members and Ad hoc Reviewers
Acknowledgement to MIR Board Members and Ad hoc Reviewers
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Acknowledgement to MIR Board Members and Ad hoc Reviewers

Management international review, 2022-02, Vol.62 (1), p.1-1 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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10
Fraunhofer Institute of Optronics Researchers Describe Findings in Machine Learning
Fraunhofer Institute of Optronics Researchers Describe Findings in Machine Learning
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Fraunhofer Institute of Optronics Researchers Describe Findings in Machine Learning

Journal of Engineering, 2023, p.784

NewsRX LLC

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