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001046033 0247_ $$2doi$$a10.1109/MIPRO65660.2025.11131987
001046033 037__ $$aFZJ-2025-03673
001046033 1001_ $$0P:(DE-HGF)0$$aCurtis, T.$$b0$$eCorresponding author
001046033 1112_ $$a2025 MIPRO 48th ICT and Electronics Convention$$cOpatija$$d2025-06-02 - 2025-06-06$$gMIPRO2025$$wCroatia
001046033 245__ $$aImproving Surface Soil Moisture Estimation with Distributed Deep Learning and HPC
001046033 260__ $$bIEEE$$c2025
001046033 300__ $$a1222-1227
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001046033 520__ $$aSoil Moisture (SM) is crucial for land surface hydrology, impacting agriculture, ecology, wildlife, and public health. This paper explores the use of Machine Learning (ML) and Deep Learning (DL) models for SM data analysis and estimation, leveraging High-Performance Computing (HPC) for efficient model training and hyperparameter tuning. The research was performed with a commercial partner and SM experts. However, utilising HPC environments requires knowledge of many low-level HPC modules (e.g., various application libraries or vendor-specific drivers like Nvidia’s CUDA, NCCL, etc.) and their specific versions needed for interoperability. Such challenges can be overcome using the Unique AI Framework (UAIF) developed in The European Center of Excellence in Exascale Computing “Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE) project. The research in this paper contributed to the co-design of several UAIF components using HPC to search for the optimal hyperparameter setup for each ML model. It compares Artificial Neural Networks (ANNs) and Recurrent Neural Networks (RNNs) to a baseline Random Forest (RF) model. Our result demonstrates a significant improvement in accuracy through distributed learning and systematic hyperparameter tuning. The findings suggest that HPC-driven DL can offer scalable, high-resolution SM predictions while leveraging the UAIF by application-domain-specific experts (e.g., SM experts, etc.), enabling easier use of HPC.
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001046033 536__ $$0G:(EU-Grant)951733$$aRAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x2
001046033 536__ $$0G:(EU-Grant)951732$$aEUROCC - National Competence Centres in the framework of EuroHPC (951732)$$c951732$$fH2020-JTI-EuroHPC-2019-2$$x3
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001046033 588__ $$aDataset connected to CrossRef Conference
001046033 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b1
001046033 7001_ $$0P:(DE-Juel1)169980$$aNeukirchen, H.$$b2
001046033 7001_ $$0P:(DE-Juel1)185652$$aBusch, J.$$b3
001046033 7001_ $$0P:(DE-Juel1)129506$$aMontzka, C.$$b4
001046033 7001_ $$0P:(DE-Juel1)180916$$aAach, Marcel$$b5
001046033 7001_ $$0P:(DE-HGF)0$$aHassanian, R.$$b6
001046033 7001_ $$0P:(DE-Juel1)178934$$aBarakat, Chadi$$b7
001046033 770__ $$z979-8-3315-3597-1
001046033 773__ $$a10.1109/MIPRO65660.2025.11131987
001046033 8564_ $$uhttps://juser.fz-juelich.de/record/1046033/files/MIPRO2025_HPC_soil_mosture.pdf$$yRestricted
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001046033 9141_ $$y2025
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