Gas source localization through deep learning method based on gas distribution map database

Kamarudin, Kamarulzaman and Mohd Juffry, Zaffry Hadi and Adom, Abdul Hamid and Miskon, Muhammad Fahmi and Ali Yeon, Ahmad Shakaff and Abdullah, Abdulnasser Nabil (2024) Gas source localization through deep learning method based on gas distribution map database. Jurnal Teknologi, UTM, 2012, 86 (2). pp. 199-208. ISSN 0127-9696

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Abstract

The incident of harmful gas leakage can cause severe damage to the environment and several casualties to human beings while the gas localization system plays a major role in mitigating those causalities. With the advances in artificial intelligence technology, deep learning is able to enhance the accuracy of the gas localization system to locate the gas source. This paper proposes a gas localization system that utilizes three different deep learning models namely DNN, 1DCNN, and 2DCNN to locate the gas source within the gas map. The proposed method involves generating the gas distribution map through the large gas sensor array platform in real-world indoor scenarios. Those models are then trained using the collected database which allows for accurate prediction of the gas source location. The performance of each proposed deep learning model was compared to find the best model demonstrating the highest effectiveness in identifying gas leaks. The study has shown that the 1DCNN has the highest effectiveness in predicting the gas source in the range between 0.0m to 0.3 m with 90.3% compared to the DNN and 2DCNN models.

Item Type:	Article
Uncontrolled Keywords:	Gas source localization, Gas distribution map, Deep learning, Harmful gas dispersion, Mobile robot olfaction
Divisions:	Faculty of Electrical Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	11 Aug 2025 04:57
Last Modified:	11 Aug 2025 04:57
URI:	http://eprints.utem.edu.my/id/eprint/28905
Statistic Details:	View Download Statistic

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