Multidimensional data generation of water distribution systems using adversarially trained autoencoder

Sehyeong Kim, Sanghoon Jun, Donghwi Jung

Research output: Contribution to journalArticlepeer-review

Abstract

Recent advancements in data measuring technology have facilitated the installation of various sensors, such as pressure meters and flow meters, to effectively assess the real-time conditions of water distribution systems (WDSs). However, as cities expand extensively, the factors that impact the reliability of measurements have become increasingly diverse. In particular, demand data, one of the most significant hydraulic variable in WDS, is challenging to be measured directly and is prone to missing values, making the development of accurate data generation models more important. Therefore, this paper proposes an adversarially trained autoencoder (ATAE) model based on generative deep learning techniques to accurately estimate demand data in WDSs. The proposed model utilizes two neural networks: a generative network and a discriminative network. The generative network generates demand data using the information provided from the measured pressure data, while the discriminative network evaluates the generated demand outputs and provides feedback to the generator to learn the distinctive features of the data. To validate its performance, the ATAE model is applied to a real distribution system in Austin, Texas, USA. The study analyzes the impact of data uncertainty by calculating the accuracy of ATAE's prediction results for varying levels of uncertainty in the demand and the pressure time series data. Additionally, the model’s performance is evaluated by comparing the results for different data collection periods (low, average, and high demand hours) to assess its ability to generate demand data based on water consumption levels.

Original languageEnglish
Pages (from-to)439-449
Number of pages11
JournalJournal of Korea Water Resources Association
Volume56
Issue number7
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Korea Water Resources Association. All rights reserved.

Keywords

  • Adversarially trained autoencoder
  • Generative adversarial networks
  • Multidimensional data generation
  • Water distribution systems

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Environmental Science (miscellaneous)
  • Ecological Modelling

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