Machine Learning Assisted Approach for Water Leaks Detection

Sara Badar*, Souad Labghough, Almaha Al-Abdulghani, Eiman Mohammed, Othmane Bouhali, Khalid A. Qaraqe

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

This study examines the use of machine learning algorithms to detect water leaks in water pipes. Multiple types of sensors have been used in a water-bed system that simulates water pipelines and leaks while gathering data. Both pressure sensors and flow sensors are employed. The obtained data is then utilized to develop an AI algorithm that can detect whether a leak occurred within the pipes based on the acquired data. We tested a number of machine learning methods to train the data and use it. These tests were conducted to evaluate the accuracy of each algorithm and determine the most effective method for predicting leaks.

Original languageEnglish
Title of host publication2023 International Conference On Information Networking, Icoin
PublisherIEEE Computer Society
Pages433-437
Number of pages5
ISBN (Electronic)9781665462686
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event37th International Conference on Information Networking, ICOIN 2023 - Bangkok, Thailand
Duration: 11 Jan 202314 Jan 2023

Publication series

NameInternational Conference On Information Networking

Conference

Conference37th International Conference on Information Networking, ICOIN 2023
Country/TerritoryThailand
CityBangkok
Period11/01/2314/01/23

Keywords

  • Ann
  • Gradient Boosting
  • Knn
  • XGBoost

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