High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
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| Publicado en: | Algorithms vol. 18, no. 4 (2025), p. 182 |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Myllis Georgios |u Department of Computer Informatics and Telecommunications Engineering, International Hellenic University, 621 24 Serres, Greece; geormyll@ihu.gr | |
| 245 | 1 | |a High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parallel computing frameworks such as Apache Spark for distributed data processing, Message Passing Interface (MPI) for fine-grained parallel execution, and CUDA-enabled GPUs for deep learning acceleration. These advancements significantly improve model training and deployment speed, enabling near-real-time data processing. Apache Spark’s in-memory computing and distributed data handling optimize data preprocessing and model execution, while MPI provides enhanced control over custom parallel algorithms, ensuring high performance in complex simulations. By leveraging these techniques, urban water utilities can implement scalable, efficient, and reliable forecasting solutions critical for sustainable water resource management in increasingly complex environments. Additionally, expanding these models to larger datasets and diverse regional contexts will be essential for validating their robustness and applicability in different urban settings. Addressing these challenges will help bridge the gap between theoretical advancements and practical implementation, ensuring that HPC-driven forecasting models provide actionable insights for real-world water management decision-making. | |
| 653 | |a Machine learning | ||
| 653 | |a Forecasting techniques | ||
| 653 | |a Accuracy | ||
| 653 | |a Water demand | ||
| 653 | |a Message passing | ||
| 653 | |a Urban environments | ||
| 653 | |a Water resources management | ||
| 653 | |a Data processing | ||
| 653 | |a Datasets | ||
| 653 | |a Deep learning | ||
| 653 | |a Forecasting | ||
| 653 | |a Water management | ||
| 653 | |a Water | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Distributed memory | ||
| 653 | |a Water utilities | ||
| 653 | |a High performance computing | ||
| 653 | |a Efficiency | ||
| 653 | |a Distributed processing | ||
| 700 | 1 | |a Tsimpiris Alkiviadis |u Department of Computer Informatics and Telecommunications Engineering, International Hellenic University, 621 24 Serres, Greece; geormyll@ihu.gr | |
| 700 | 1 | |a Aggelopoulos Stamatios |u Department of Agriculture, International Hellenic University, Thessaloniki, 570 01 Nea Moudania, Greece; saggelopoulos@ihu.gr | |
| 700 | 1 | |a Vrana, Vasiliki G |u Department of Business Administration, International Hellenic University, 621 24 Serres, Greece | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 4 (2025), p. 182 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194484919/abstract/embedded/Y2VX53961LHR7RE6?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194484919/fulltextwithgraphics/embedded/Y2VX53961LHR7RE6?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194484919/fulltextPDF/embedded/Y2VX53961LHR7RE6?source=fedsrch |