Result Number | Material Type | Add to My Shelf Action | Record Details and Options |
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1 |
Material Type: Artigo
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Deep learning shows declining groundwater levels in Germany until 2100 due to climate changeWunsch, Andreas ; Liesch, Tanja ; Broda, StefanNature communications, 2022-03, Vol.13 (1), p.1221-1221, Article 1221 [Periódico revisado por pares]England: Nature Publishing GroupTexto completo disponível |
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2 |
Material Type: Artigo
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Karst spring discharge modeling based on deep learning using spatially distributed input dataWunsch, Andreas ; Liesch, Tanja ; Cinkus, Guillaume ; Ravbar, NataÅ¡a ; Chen, Zhao ; Mazzilli, Naomi ; Jourde, Hervé ; Goldscheider, NicoHydrology and earth system sciences, 2022-05, Vol.26 (9), p.2405-2430 [Periódico revisado por pares]Katlenburg-Lindau: Copernicus GmbHTexto completo disponível |
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3 |
Material Type: Artigo
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Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practicesKarimanzira, Divas ; Weis, Jonas ; Wunsch, Andreas ; Ritzau, Linda ; Liesch, Tanja ; Ohmer, MarcFrontiers in water, 2023-07, Vol.5 [Periódico revisado por pares]Frontiers Media S.ATexto completo disponível |
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4 |
Material Type: Artigo
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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, StefanHydrology and earth system sciences, 2021-04, Vol.25 (3), p.1671-1687 [Periódico revisado por pares]Katlenburg-Lindau: Copernicus GmbHTexto completo disponível |
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5 |
Material Type: Artigo
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Groundwater level forecasting with artificial neural networks: a comparison of long short-term memoryWunsch, Andreas ; Liesch, Tanja ; Broda, StefanHydrology and earth system sciences, 2021-04, Vol.25 (3), p.1671 [Periódico revisado por pares]Copernicus GmbHTexto completo disponível |
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6 |
Material Type: Artigo
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On the challenges of global entity-aware deep learning models for groundwater level predictionHeudorfer, Benedikt ; Liesch, Tanja ; Broda, StefanHydrology and earth system sciences, 2024-02, Vol.28 (3), p.525-543 [Periódico revisado por pares]Katlenburg-Lindau: Copernicus GmbHTexto completo disponível |
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7 |
Material Type: Artigo
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Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methodsOhmer, Marc ; Liesch, Tanja ; Wunsch, AndreasHydrology and earth system sciences, 2022-08, Vol.26 (15), p.4033-4053 [Periódico revisado por pares]Katlenburg-Lindau: Copernicus GmbHTexto completo disponível |
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8 |
Material Type: Artigo
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When best is the enemy of good – critical evaluation of performance criteria in hydrological modelsCinkus, Guillaume ; Mazzilli, Naomi ; Jourde, Hervé ; Wunsch, Andreas ; Liesch, Tanja ; Ravbar, NataÅ¡a ; Chen, Zhao ; Goldscheider, NicoHydrology and earth system sciences, 2023-07, Vol.27 (13), p.2397-2411 [Periódico revisado por pares]Katlenburg-Lindau: Copernicus GmbHTexto completo disponível |
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9 |
Material Type: Artigo
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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 regionsCinkus, 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, HervéHydrology and earth system sciences, 2023-05, Vol.27 (10), p.1961-1985 [Periódico revisado por pares]Katlenburg-Lindau: Copernicus GmbHTexto completo disponível |