Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Muhammad Iqmmal Rezzwan Radzman
University of Tun Hussein Onn Malaysia
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Pipe leakage detection system with artificial neural network Muhammad Iqmmal Rezzwan Radzman; Abd Kadir Mahamad; Siti Zarina Mohd Muji; Sharifah Saon; Mohd Anuaruddin Ahmadon; Shingo Yamaguchi; Muhammad Ikhsan Setiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp977-985

Abstract

This project aims to develop a system that can monitor to detect leaks in water distribution networks. It has been projected that leakage from pipelines may lead to significant economic losses and environmental damage. The loss of water from leaks in pipeline systems accounts for a large portion of the water supply. Pipelines are maintained throughout their lives span; however, it is difficult to avoid a leak occurring at some point. A tremendous amount of water could be saved globally if automated leakage detection systems were introduced. An embedded system that monitors water leaks can efficiently aid in water conservation. This project focuses on developing a real-time water leakage detection system using a few types of sensors: water flow rate sensor, vibration sensor, and water pressure sensor. The data from the sensors is uploaded and stored by the microcontroller (NodeMCU V3) to the database cloud (Google Sheets). The data that is stored in the database is analyzed by artificial neural network (ANN) by using Matlab software. An application is developed based on results from ANN training to detect the leakage event. Implementing the proposed system can increase operations efficiency, reduce delay times, and reduce maintenance costs after leaks are detected.
Co-Authors Abd Kadir Mahamad Mohd Anuaruddin Ahmadon Muhammad Ikhsan Setiawan Sharifah Saon Shingo Yamaguchi Siti Zarina Mohd Muji