Integrated dynamic multi-threshold pattern recognition with graph attention long short-term neural memory network for water distribution network losses prediction: An automated expert system

Minglei Fu, Qi Zhang, Kezhen Rong, Zaher Mundher Yaseen*, Lejin Zheng, Jianfeng Zheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Water loss is a common and critical problem in water distribution networks, resulting in a decrease in wastewater and user experience. In this research, prediction-classification framework based on deep learning (i.e., graph attention long short-term neural memory network (GA-LSTM)) is proposed. Dynamic multi-threshold pattern recognition (DMTPR) was developed to identify and classify losses. To improve the model's reliability, we performed hyperparameter optimisation, trained and verified using one year's operation data of a real pipe network, and compared it with the previous methods. In addition, the influence of different aggregation ranges on the accuracy and stability of the model was analysed. The results showed that the GA-LSTM-DMTPR framework can be used as a reliable tool for practical applications because it provides high-precision and high-stability water demand prediction and realises accurate loss identification and classification. This is owing the potential of the model to aggregate the spatial and temporal multi-dimensional information.

Original languageEnglish
Article number107277
JournalEngineering Applications of Artificial Intelligence
Volume127
DOIs
StatePublished - Jan 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Deep learning
  • Losses detection
  • Pattern recognition
  • Sustainable development
  • Water natural resources

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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