@inproceedings{6ec26a95ff9a4f19a724753eff334a70,
title = "Machine Learning Assisted Approach for Water Leaks Detection",
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.",
keywords = "Ann, Gradient Boosting, Knn, XGBoost",
author = "Sara Badar and Souad Labghough and Almaha Al-Abdulghani and Eiman Mohammed and Othmane Bouhali and Qaraqe, {Khalid A.}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 37th International Conference on Information Networking, ICOIN 2023 ; Conference date: 11-01-2023 Through 14-01-2023",
year = "2023",
doi = "10.1109/ICOIN56518.2023.10048954",
language = "English",
series = "International Conference On Information Networking",
publisher = "IEEE Computer Society",
pages = "433--437",
booktitle = "2023 International Conference On Information Networking, Icoin",
address = "United States",
}