# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 4 Conclusions  
This study presents an integrated high-performance computing (HPC) pipeline that combines Apache Spark, Message Passing Interface (MPI), and GPU acceleration for efficient urban water demand forecasting. By leveraging parallel computing techniques, we demonstrate substantial computational improvements, reducing execution times by up to 5.7\(\times\) for deep learning tasks and 5.3\(\times\) for ensemble-based forecasting models. The results confirm that parallelized execution and GPU acceleration significantly enhance the scalability and efficiency of complex machine learning workflows in water management applications. The computational analysis highlights key insights into parallel efficiency, throughput, and resource utilization. Bi-LSTM optimization achieved the highest efficiency (71.12%), demonstrating that deep learning workloads effectively leverage parallel execution. Meanwhile, Random Forest training required substantial memory (512 MiB) and shuffle I/O (300 MiB), indicating that tree-based models impose a larger computational footprint compared to LSTM-based approaches. The results further reveal that parallel computing primarily increases throughput without reducing per-task latency, reinforcing the need for optimized task scheduling and resource management in HPC environments. From an operational perspective, the integration of Spark, MPI, and GPU-based deep learning enables near-real-time forecasting capabilities, ensuring timely decision-making in urban water resource management. The ability to reduce end-to-end pipeline execution from 8 h to just 9 min (inference with pre-trained models) demonstrates the feasibility of real-time deployment in large-scale infrastructure settings. Moreover, the robustness of the proposed system, validated through Spark History Server logs and MPI execution traces, ensures resilience to system failures and computational inefficiencies. Compared to existing studies that emphasize forecasting accuracy, this study prioritizes scalability and computational efficiency. While prior research has introduced more complex forecasting architectures, their practical deployment is often constrained by computational costs. Our findings bridge this gap by demonstrating how existing models can be optimized for real-time execution using HPC frameworks without sacrificing predictive performance. The integration of Spark for distributed data preprocessing, MPI for parallel execution, and CUDA-enabled GPUs for deep learning ensures that state-of-the-art forecasting models can be operationalized effectively at scale. Despite these advancements, certain limitations remain. The study was conducted on a single-node HPC setup, limiting scalability to eight CPU cores and two GPUs. Future work should explore multi-node deployments and cloud-based HPC solutions to assess the pipeline's performance under distributed computing environments. Additionally, further optimization in hyperparameter tuning, possibly through transfer learning or multi-fidelity optimization, could reduce computational overhead while maintaining forecasting accuracy. Overall, this research provides a scalable, efficient, and fault-tolerant forecasting solution for urban water management. The demonstrated computational gains validate the integration of parallel computing with deep learning techniques, making real-time forecasting feasible for large-scale water systems. Future research should focus on extending these methodologies to broader environmental applications, ensuring sustainable and data-driven resource management in complex urban infrastructures. Conceptualization, G.M. and A.T.; methodology, G.M. and A.T.; software, G.M. ; validation, G.M. and A.T.; formal analysis, G.M. ; investigation, G.M. ; resources, G.M. ; data curation, G.M. ; writing--original draft preparation, G.M. ; writing--review and editing, G.M., A.T.S.A. and V.G.V. ; visualization, A.T. and V.G.V. ; supervision, A.T., S.A. and V.G.V. ; project administration, A.T., S.A. and V.G.V.