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@INPROCEEDINGS{Curtis:1046033,
      author       = {Curtis, T. and Riedel, Morris and Neukirchen, H. and Busch,
                      J. and Montzka, C. and Aach, Marcel and Hassanian, R. and
                      Barakat, Chadi},
      title        = {{I}mproving {S}urface {S}oil {M}oisture {E}stimation with
                      {D}istributed {D}eep {L}earning and {HPC}},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-03673},
      pages        = {1222-1227},
      year         = {2025},
      abstract     = {Soil Moisture (SM) is crucial for land surface hydrology,
                      impacting agriculture, ecology, wildlife, and public health.
                      This paper explores the use of Machine Learning (ML) and
                      Deep Learning (DL) models for SM data analysis and
                      estimation, leveraging High-Performance Computing (HPC) for
                      efficient model training and hyperparameter tuning. The
                      research was performed with a commercial partner and SM
                      experts. However, utilising HPC environments requires
                      knowledge of many low-level HPC modules (e.g., various
                      application libraries or vendor-specific drivers like
                      Nvidia’s CUDA, NCCL, etc.) and their specific versions
                      needed for interoperability. Such challenges can be overcome
                      using the Unique AI Framework (UAIF) developed in The
                      European Center of Excellence in Exascale Computing
                      “Research on AI- and Simulation-Based Engineering at
                      Exascale” (CoE RAISE) project. The research in this paper
                      contributed to the co-design of several UAIF components
                      using HPC to search for the optimal hyperparameter setup for
                      each ML model. It compares Artificial Neural Networks (ANNs)
                      and Recurrent Neural Networks (RNNs) to a baseline Random
                      Forest (RF) model. Our result demonstrates a significant
                      improvement in accuracy through distributed learning and
                      systematic hyperparameter tuning. The findings suggest that
                      HPC-driven DL can offer scalable, high-resolution SM
                      predictions while leveraging the UAIF by
                      application-domain-specific experts (e.g., SM experts,
                      etc.), enabling easier use of HPC.},
      month         = {Jun},
      date          = {2025-06-02},
      organization  = {2025 MIPRO 48th ICT and Electronics
                       Convention, Opatija (Croatia), 2 Jun
                       2025 - 6 Jun 2025},
      cin          = {JSC / IBG-3},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IBG-3-20101118},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5111 - Domain-Specific
                      Simulation $\&$ Data Life Cycle Labs (SDLs) and Research
                      Groups (POF4-511) / RAISE - Research on AI- and
                      Simulation-Based Engineering at Exascale (951733) / EUROCC -
                      National Competence Centres in the framework of EuroHPC
                      (951732) / BMBF 01ZZ1803B - SMITH -
                      Medizininformatik-Konsortium - Beitrag Universitätsklinikum
                      Aachen (01ZZ1803B)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111 /
                      G:(EU-Grant)951733 / G:(EU-Grant)951732 / G:(BMBF)01ZZ1803B},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1109/MIPRO65660.2025.11131987},
      url          = {https://juser.fz-juelich.de/record/1046033},
}