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zmla_2006.nc
Plain Text - 911.4 MB - SHA-256: 833541232ca117cf30cbb11e831b956cb6ed1be6387f8a8806c53ea3849b4fd0
zmla_2007.nc
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zmla_2008.nc
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zmla_2009.nc
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zmla_2010.nc
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zmla_2011.nc
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zmla_2012.nc
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zmla_2013.nc
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zmla_2014.nc
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Advancing AI-based pan-European groundwater monitoring
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...
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