{"dcterms:modified":"2022-03-28","dcterms:creator":"A Dataverse Instance","@type":"ore:ResourceMap","@id":"https://data.fz-juelich.de/api/datasets/export?exporter=OAI_ORE&persistentId=doi:10.26165/JUELICH-DATA/ZBLDIR","ore:describes":{"Title":"Advancing AI-based pan-European groundwater monitoring","fzj:Institute":"IBG-3","fzj:PoF IV topic":"Agro-biogeosystems: controls, feedbacks and impact (POF4-2173)","Author":[{"author:Name":"Ma, Yueling","author:Affiliation":"Forschungszentrum Jülich","Identifier Scheme":"ORCID","Identifier":"https://orcid.org/ 0000-0002-1869-7702"},{"author:Name":"Montzka, Carsten","author:Affiliation":"Forschungszentrum Jülich"},{"author:Name":"Naz, Bibi","author:Affiliation":"Forschungszentrum Jülich"},{"author:Name":"Kollet, Stefan","author:Affiliation":"Forschungszentrum Jülich"}],"citation:Contact":{"datasetContact:Name":"Ma, Yueling","datasetContact:Affiliation":"Forschungszentrum Jülich","datasetContact:E-mail":"y.ma@fz-juelich.de"},"citation:Description":{"dsDescription:Text":"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 named LSTM-TL. The data repository provides: i) data utilized for evaluating LSTM-TL performance, i.e., input averaged monthly precipitation and soil moisture anomalies from common observational data sets (pr_a,o and θ_a,o), wtd_a estimates obtained from LSTM-TL (wtd_a,lstm-tl), wtd_a estimates obtained from LSTM networks with modeling data as input (wtd_a,lstm(m)), and wtd_a estimates obtained from LSTM networks trained on observations (wtd_a,lstm(o)); ii) reconstructed European monthly wtd_a,lstm-tl data RD1-6 from the early 1980s to the near present and their input pr_a,o and θ_a,o data; and iii) a Jupyter Notebook showing an example about the implementation of LSTM-TL. All the data sets in the repository have a spatial resolution of 0.11 degrees (~12.5 km).","dsDescription:Date":"2021-12-13"},"Subject":"Earth and Environmental Sciences","citation:Keyword":[{"keyword:Term":"Groundwater"},{"keyword:Term":"Anomalies"},{"keyword:Term":"LSTM-TL"},{"keyword:Term":"Long Short-Term Memory (LSTM) networks"},{"keyword:Term":"Transfer learning (TL)"},{"keyword:Term":"Europe"}],"Related Publication":{"Citation":"Ma, Y., Montzka, C., Naz, B. and Kollet, S.: Advancing AI-based pan-European groundwater monitoring, in preparation."},"citation:Depositor":"Ma, Yueling","Deposit Date":"2021-12-13","@id":"doi:10.26165/JUELICH-DATA/ZBLDIR","@type":["ore:Aggregation","schema:Dataset"],"schema:version":"2.0","schema:datePublished":"2021-12-14","schema:name":"Advancing AI-based pan-European groundwater monitoring","schema:dateModified":"2022-03-28 07:12:47.936","schema:license":"https://creativecommons.org/publicdomain/zero/1.0/","dvcore:fileTermsOfAccess":{"dvcore:fileRequestAccess":false},"schema:includedInDataCatalog":"Campus Collection","ore:aggregates":[{"schema:description":"Monthly precipitation anomalies (pr_a) derived from the COSMO-REA6 dataset (referred to: \r\nBollmeyer, C., Keller, J. 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