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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Quantification and possible causes of declining groundwater resources in the Euro-Mediterranean region from 2003 to 2020Xanke, Julian ; Liesch, TanjaHydrogeology journal, 2022-03, Vol.30 (2), p.379-400 [Periódico revisado por pares]Berlin/Heidelberg: Springer Berlin HeidelbergTexto completo disponível |
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2 |
Material Type: Artigo
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Feature-based Groundwater Hydrograph Clustering Using Unsupervised Self-Organizing Map-EnsemblesWunsch, Andreas ; Liesch, Tanja ; Broda, StefanWater resources management, 2022, Vol.36 (1), p.39-54 [Periódico revisado por pares]Dordrecht: Springer NetherlandsTexto completo disponível |
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3 |
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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4 |
Material Type: Artigo
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Comparison of GRACE data and groundwater levels for the assessment of groundwater depletion in JordanLiesch, Tanja ; Ohmer, MarcHydrogeology journal, 2016-09, Vol.24 (6), p.1547-1563 [Periódico revisado por pares]Berlin/Heidelberg: Springer Berlin HeidelbergTexto completo disponível |
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5 |
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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6 |
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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7 |
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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8 |
Material Type: Artigo
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Uranium in groundwater — Fertilizers versus geogenic sourcesLiesch, Tanja ; Hinrichsen, Sören ; Goldscheider, NicoThe Science of the total environment, 2015-12, Vol.536, p.981-995 [Periódico revisado por pares]Netherlands: Elsevier B.VTexto completo disponível |
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9 |
Material Type: Artigo
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Influence of sediments burying the discharge area of a karst aquifer on the groundwater flow field—Numerical testing of conceptual modelsOhmer, Marc ; Liesch, Tanja ; Goldscheider, NicoHydrological processes, 2023-12, Vol.37 (12), p.n/a [Periódico revisado por pares]Hoboken, USA: John Wiley & Sons, IncTexto completo disponível |
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10 |
Material Type: Artigo
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Global analysis of land-use changes in karst areas and the implications for water resourcesZhang, Jiawen ; Liesch, Tanja ; Chen, Zhao ; Goldscheider, NicoHydrogeology journal, 2023-08, Vol.31 (5), p.1197-1208 [Periódico revisado por pares]Berlin/Heidelberg: Springer Berlin HeidelbergTexto completo disponível |