Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System
Detecting gas leaks in gas plants is a persistent challenge within the Oil and Gas industry, given the prevalence of pipelines for natural gas transportation. Therefore, various traditional techniques are used for gas leakage detection system, however, conventional methods possess various limitatio...
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| Format: | Article |
| Language: | English |
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Centro Latinoamericano de Estudios en Informática
2025-06-01
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| Series: | CLEI Electronic Journal |
| Online Access: | https://www.clei.org/cleiej/index.php/cleiej/article/view/840 |
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| author | Kondireddy Muni Sankar Dr. B. Booba |
| author_facet | Kondireddy Muni Sankar Dr. B. Booba |
| author_sort | Kondireddy Muni Sankar |
| collection | DOAJ |
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Detecting gas leaks in gas plants is a persistent challenge within the Oil and Gas industry, given the prevalence of pipelines for natural gas transportation. Therefore, various traditional techniques are used for gas leakage detection system, however, conventional methods possess various limitations like time consuming, prone to error and can be tedious to work with. Hence, AI based models are preferred for effec tive gas leakage detection as AI (Artificial Intelligence) based ML (Machine Learning) models generate tremendous advantages in terms of accuracy of gas leakage detection, early detection and being cost effective approaches. Thus present research work focuses on evaluating intelligent models' efficacy in identifying minor leaks in gas pipelines with fundamental operational parameters. The research then proceeds to compare these models using established performance metrics. The ML based models used in the research work are Linear Regression, Logistic Regression, RF (Random Forest) and KNN (K-Nearest Neighbor). The following ML based algorithms are compared and the performance of the model was evaluated using assorted metrics in accordance with different types of damages. Present comparative research work can potentially assist different industrial sectors for identifying gas leaks.
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| format | Article |
| id | doaj-art-2e5b9ed176894753a9e1ee4a0ea8dc5d |
| institution | DOAJ |
| issn | 0717-5000 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Centro Latinoamericano de Estudios en Informática |
| record_format | Article |
| series | CLEI Electronic Journal |
| spelling | doaj-art-2e5b9ed176894753a9e1ee4a0ea8dc5d2025-08-20T02:39:29ZengCentro Latinoamericano de Estudios en InformáticaCLEI Electronic Journal0717-50002025-06-0128310.19153/cleiej.28.3.12Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage SystemKondireddy Muni Sankar0Dr. B. BoobaResearch Scholar, Department of Information Technology, Vels Institute of Science, Technology, and Advanced Studies (VISTAS),Chennai, India. Detecting gas leaks in gas plants is a persistent challenge within the Oil and Gas industry, given the prevalence of pipelines for natural gas transportation. Therefore, various traditional techniques are used for gas leakage detection system, however, conventional methods possess various limitations like time consuming, prone to error and can be tedious to work with. Hence, AI based models are preferred for effec tive gas leakage detection as AI (Artificial Intelligence) based ML (Machine Learning) models generate tremendous advantages in terms of accuracy of gas leakage detection, early detection and being cost effective approaches. Thus present research work focuses on evaluating intelligent models' efficacy in identifying minor leaks in gas pipelines with fundamental operational parameters. The research then proceeds to compare these models using established performance metrics. The ML based models used in the research work are Linear Regression, Logistic Regression, RF (Random Forest) and KNN (K-Nearest Neighbor). The following ML based algorithms are compared and the performance of the model was evaluated using assorted metrics in accordance with different types of damages. Present comparative research work can potentially assist different industrial sectors for identifying gas leaks. https://www.clei.org/cleiej/index.php/cleiej/article/view/840 |
| spellingShingle | Kondireddy Muni Sankar Dr. B. Booba Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System CLEI Electronic Journal |
| title | Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System |
| title_full | Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System |
| title_fullStr | Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System |
| title_full_unstemmed | Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System |
| title_short | Performance Evaluation of Machine Learning Algorithms for Detecting Gas Leakage System |
| title_sort | performance evaluation of machine learning algorithms for detecting gas leakage system |
| url | https://www.clei.org/cleiej/index.php/cleiej/article/view/840 |
| work_keys_str_mv | AT kondireddymunisankar performanceevaluationofmachinelearningalgorithmsfordetectinggasleakagesystem AT drbbooba performanceevaluationofmachinelearningalgorithmsfordetectinggasleakagesystem |