Development of cross-domain artificial neural network to predict high-temporal resolution pressure data

Young Hwan Choi, Donghwi Jung

Research output: Contribution to journalArticlepeer-review


Forecasting hydraulic data such as pressure and demand in water distribution system (WDS) is an important task that helps ensure efficient and accurate operations. Despite high-performance data prediction, missing data can still occur, making it difficult to effectively operate WDS. Though the pressure data are directly related to the rules of operation for pumps or valves, few studies have been conducted on pressure data forecasting. This study proposes a new missing and incomplete data control approach based on real pressure data for reliable and efficient WDSoperation and maintenance. The proposed approach is: (1) application of source data from high-resolution, real-world pressure data; (2) development of a cross-domain artificial neural network (CDANN), combining the standard artificial neural networks (ANNs) and the cross-domain training approach for missing data control; and (3) analysis of standard data mining according to external factors to improve prediction accuracy. To verify the proposed approach, a real-world network located in South Korea was used, and the forecasting results were evaluated through performance indicators (i.e., overall, special points, and percentage errors). The performance of the CDANN is compared with that of standard ANNs, and CDANN was found to provide better predictions than traditional ANNs.

Original languageEnglish
Article number3832
JournalSustainability (Switzerland)
Issue number9
Publication statusPublished - 2020 May 1

Bibliographical note

Funding Information:
Funding: This work was supported (1) by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1C1C1006481) and (2) by a Korea University Grant.

Publisher Copyright:
© 2020 by the authors.


  • Cross-domain artificial neural network
  • Data categorization standard
  • Missing data control
  • Pressure data prediction
  • Water distribution system

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Computer Networks and Communications
  • Management, Monitoring, Policy and Law


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