Edge Computing in Water Management: A KPCA-DeepESN and HOA-Optimized Framework for Urban Resource Allocation

This paper presents a novel approach to optimizing urban water resource allocation by integrating Kernel Principal Component Analysis (KPCA) with a Deep Echo State Network (DeepESN), further optimized using the Hiking Optimization Algorithm (HOA). The proposed model addresses the issue of achieving an optimal balance between water supply and demand in urban environments, utilizing advanced machine learning techniques to enhance prediction accuracy and allocation efficiency. KPCA is employed to reduce the dimensionality of key water resource indicators, capturing nonlinear relationships in the dataset. DeepESN, a deep recurrent neural network model, is then applied to predict water consumption trends. HOA, a meta-heuristic algorithm inspired by hiker behavior, is used to fine-tune the DeepESN network parameters, ensuring faster convergence and higher accuracy. The experimental setup includes water resource data from January 2010 to December 2023, divided into training, testing, and validation sets. The model’s performance is compared with other approaches, such as PCA-DeepESN and standalone DeepESN. Results show that the KPCA-HOA-DeepESN model achieves the lowest prediction error and fastest convergence, making it a superior solution for urban water management. Optimized network parameters include a reservoir size of 140, a spectral radius of 0.3, an input scaling factor of 0.22, and a reservoir sparsity degree of 0.72. This study demonstrates the applicability of distributed computing techniques in water resource management by utilizing cloud-based data processing and real-time predictions. The proposed approach not only improves resource allocation but also showcases the potential for edge computing to enhance the responsiveness of water management systems.

@article{Liao2024,
  title     = {Edge Computing in Water Management: A KPCA-DeepESN and HOA-Optimized Framework for Urban Resource Allocation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {11},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Hanchao Liao and Miyuan Shan},
  doi       = {10.14569/IJACSA.2024.0151155},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151155}
}