Machine learning for water supply supervision

Thomas Schranz, Gerald Schweiger, Siegfried Pabst, Franz Wotawa*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

In an industrial setting water supply systems can be complex. Constructing physical models for fault diagnosis or prediction requires extensive knowledge about the system’s components and characteristics. Through advances in embedded computing, consumption meter data is often readily available. This data can be used to construct black box models that describe system behavior and highlight irregularities such as leakages. In this paper we discuss the application of artificial intelligence to the task of identifying irregular consumption patterns. We describe and evaluate data models based on neural networks and decision trees that were used for consumption prediction in buildings at the Graz University of Technology.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
EditorsHamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
PublisherSpringer Science and Business Media Deutschland GmbH
Pages238-249
Number of pages12
ISBN (Print)9783030557881
DOIs
Publication statusPublished - 1 Jan 2020
Event33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems - Hybrider Event, Japan
Duration: 22 Sep 202025 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12144 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems
Abbreviated titleIEA/AIE 2020
Country/TerritoryJapan
CityHybrider Event
Period22/09/2025/09/20

Keywords

  • Data science
  • Fault diagnosis
  • Machine learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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