| Forecasting groundwater levels using nonlinear autoregressive networks with exogenous input (NARX) A Wunsch, T Liesch, S Broda Journal of Hydrology 567, 743-758, 2018 | 223 | 2018 |
| Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear … A Wunsch, T Liesch, S Broda Hydrology and Earth System Sciences 25 (3), 1671-1687, 2021 | 220 | 2021 |
| Deep learning shows declining groundwater levels in Germany until 2100 due to climate change A Wunsch, T Liesch, S Broda Nature communications 13 (1), 1221, 2022 | 131 | 2022 |
| Karst modelling challenge 1: Results of hydrological modelling PY Jeannin, G Artigue, C Butscher, Y Chang, JB Charlier, L Duran, L Gill, ... Journal of Hydrology 600, 126508, 2021 | 71 | 2021 |
| Karst spring discharge modeling based on deep learning using spatially distributed input data A Wunsch, T Liesch, G Cinkus, N Ravbar, Z Chen, N Mazzilli, H Jourde, ... Hydrology and Earth System Sciences 26 (9), 2405-2430, 2022 | 43 | 2022 |
| Aquifer responses to long-term climatic periodicities T Liesch, A Wunsch Journal of Hydrology 572, 226-242, 2019 | 40 | 2019 |
| Feature-based groundwater hydrograph clustering using unsupervised self-organizing map-ensembles A Wunsch, T Liesch, S Broda Water Resources Management 36 (1), 39-54, 2022 | 27 | 2022 |
| Groundwater level forecasting with artificial neural networks: A comparison of LSTM, CNN and NARX A Wunsch, T Liesch, S Broda Hydrol. Earth Syst. Sci. Discuss 552, 1-23, 2020 | 25 | 2020 |
| Deep learning shows declining groundwater levels in Germany until 2100 due to climate change, Nat. Commun., 13, 1221 A Wunsch, T Liesch, S Broda | 23 | 2022 |
| Comparison of artificial neural networks and reservoir models for simulating karst spring discharge on five test sites in the Alpine and Mediterranean regions G Cinkus, A Wunsch, N Mazzilli, T Liesch, Z Chen, N Ravbar, J Doummar, ... Hydrology and Earth System Sciences Discussions 2022, 1-41, 2022 | 19 | 2022 |
| When best is the enemy of good–critical evaluation of performance criteria in hydrological models G Cinkus, N Mazzilli, H Jourde, A Wunsch, T Liesch, N Ravbar, Z Chen, ... Hydrology and Earth System Sciences 27 (13), 2397-2411, 2023 | 18 | 2023 |
| Deep learning shows declining groundwater levels in Germany until 2100 due to climate change, Nat. Commun., 13, 1–13 A Wunsch, T Liesch, S Broda | 8 | 2022 |
| Spatiotemporal optimization of groundwater monitoring networks using data-driven sparse sensing methods M Ohmer, T Liesch, A Wunsch Hydrology and Earth System Sciences 26 (15), 4033-4053, 2022 | 7 | 2022 |
| Towards understanding the influence of seasons on low groundwater periods based on explainable machine learning A Wunsch, T Liesch, N Goldscheider Hydrology and Earth System Sciences Discussions 2023, 1-19, 2023 | 5 | 2023 |
| Application of machine learning and deep neural networks for spatial prediction of groundwater nitrate concentration to improve land use management practices D Karimanzira, J Weis, A Wunsch, L Ritzau, T Liesch, M Ohmer Frontiers in Water 5, 1193142, 2023 | 5 | 2023 |
| Nitrat-Monitoring 4.0–Intelligente Systeme zur nachhaltigen Reduzierung von Nitrat im Grundwasser T Liesch, J Bruns, A Abecker, D Hilbring, D Karimanzira, T Martin, ... INFORMATIK 2020, 1069-1079, 2021 | 3 | 2021 |
| Entwicklung und Anwendung von Algorithmen zur Berechnung von Grundwasserständen an Referenzmessstellen auf Basis der Methode Künstlicher Neuronaler Netze: Abschlussbericht A Wunsch, T Liesch | 2 | 2020 |
| Karst spring discharge modeling based on deep learning using spatially distributed input data A Wunsch, T Liesch, G Cinkus, N Ravbar, Z Chen, N Mazzilli, H Jourde, ... Hydrology and Earth System Sciences Discussions 2021, 1-26, 2021 | 1 | 2021 |
| Modeling the discharge behavior of an alpine karst spring influenced by seasonal snow accumulation and melting based on a deep-learning approach T Liesch, A Wunsch, Z Chen, N Goldscheider EGU General Assembly Conference Abstracts, EGU21-12181, 2021 | 1 | 2021 |
| Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge RA Collenteur, E Haaf, M Bakker, T Liesch, A Wunsch, ... Hydrology and Earth System Sciences 28 (23), 5193-5208, 2024 | | 2024 |
Tanja LieschSenior Hydrogeologist, Karlsruhe Institute of Technology KITE-mail megerősítve itt: kit.edu