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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
Autor principal: Myllis Georgios
Otros Autores: Tsimpiris Alkiviadis, Aggelopoulos Stamatios, Vrana, Vasiliki G
Publicado:
MDPI AG
Materias:
Machine learning
Forecasting techniques
Accuracy
Water demand
Message passing
Urban environments
Water resources management
Data processing
Datasets
Deep learning
Forecasting
Water management
Water
Algorithms
Real time
Distributed memory
Water utilities
High performance computing
Efficiency
Distributed processing
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1999-4893
DOI:10.3390/a18040182
Fuente:Engineering Database