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Plain Text - 911.4 MB - SHA-256: 90d09604a7cbb383749a744b0361f0b2dab6cb39fe118c953aa980fa2eb87347
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Mar 28, 2022
Ma, Yueling; Montzka, Carsten; Naz, Bibi; Kollet, Stefan, 2021, "Advancing AI-based pan-European groundwater monitoring", https://doi.org/10.26165/JUELICH-DATA/ZBLDIR, Jülich DATA, V2
This study proposes an AI-based methodology combining Long Short-Term Memory (LSTM) networks and transfer learning (TL) to estimate water table depth anomalies (wtd_a) at the European scale in the absence of consistent water table depth (wtd) observational data sets, which is nam...
Jupyter Notebook - 82.0 KB - SHA-256: 9fae22a6005caf9cb3361e20ba0bd5954550e833e3444ef24a917abe41a77b5b
A Jupyter Notebook showing an example about the implementation of LSTM-TL
Network Common Data Form - 197.3 MB - SHA-256: 93abe0a605585b816742fe50b77030254c284fd236ea4319b5d7216c1bbce371
Monthly precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: Bollmeyer, C., Keller, J. D., Ohlwein, C., Wahl, S., Crewell, S., Friederichs, P., Hense, A., Keune, J., Kneifel, S., Pscheidt, I., Redl, S. and Steinke, S.: Towards a high‐resolution regio...
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