ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification
Abstract Gas leakage detection is imperative in various sectors, including chemical industries, coal mines, and household applications. The escalating number of accidents in coal mines, chemical industries, and homes underscores the urgency of swift and accurate gas detection methods. This research...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00742-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832571285116289024 |
|---|---|
| author | B. Lalithadevi S. Krishnaveni |
| author_facet | B. Lalithadevi S. Krishnaveni |
| author_sort | B. Lalithadevi |
| collection | DOAJ |
| description | Abstract Gas leakage detection is imperative in various sectors, including chemical industries, coal mines, and household applications. The escalating number of accidents in coal mines, chemical industries, and homes underscores the urgency of swift and accurate gas detection methods. This research focuses on developing advanced systems that promptly identify gas types to prevent harm to human lives and the environment. This paper addresses the challenges of gas leakage detection and classification in diverse environments, such as industrial, residential, and mining scenarios. The proposed ExAIRFC-GSDC model integrates machine learning algorithms, particularly a Random Forest Classifier, with explainable artificial intelligence (XAI) techniques to enhance interpretability. This study employs a dataset comprising gas sensor measurements that encompassing gasses, such as Liquid Petroleum Gas (LPG), Compressed Natural Gas (CNG), Methane, Propane, and others. Various machine learning classifiers, including K-Nearest Neighbors, Decision Tree, Support Vector Machines, XGBoost, and others, are compared with ExAIRFC-GSDC for gas detection. The model demonstrates superior performance, achieving an accuracy rate of 98.67%. Incorporating SHAP and LIME explanations enhances the model's interpretability, providing insights into the contributions of individual sensors. Statistical analysis confirms the significant differences in sensor readings across different gas types. ExAIRFC-GSDC is a robust and explainable solution for accurate gas detection and classification in complex environments. |
| format | Article |
| id | doaj-art-19e56e25df5b4024a8380da29138f238 |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-19e56e25df5b4024a8380da29138f2382025-02-02T12:41:58ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-01-0118113310.1007/s44196-025-00742-6ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and ClassificationB. Lalithadevi0S. Krishnaveni1Department of Computer Science and Engineering, Sathyabama Institute of Science and TechnologyDepartment of Computational Intelligence, SRM Institute of Science and TechnologyAbstract Gas leakage detection is imperative in various sectors, including chemical industries, coal mines, and household applications. The escalating number of accidents in coal mines, chemical industries, and homes underscores the urgency of swift and accurate gas detection methods. This research focuses on developing advanced systems that promptly identify gas types to prevent harm to human lives and the environment. This paper addresses the challenges of gas leakage detection and classification in diverse environments, such as industrial, residential, and mining scenarios. The proposed ExAIRFC-GSDC model integrates machine learning algorithms, particularly a Random Forest Classifier, with explainable artificial intelligence (XAI) techniques to enhance interpretability. This study employs a dataset comprising gas sensor measurements that encompassing gasses, such as Liquid Petroleum Gas (LPG), Compressed Natural Gas (CNG), Methane, Propane, and others. Various machine learning classifiers, including K-Nearest Neighbors, Decision Tree, Support Vector Machines, XGBoost, and others, are compared with ExAIRFC-GSDC for gas detection. The model demonstrates superior performance, achieving an accuracy rate of 98.67%. Incorporating SHAP and LIME explanations enhances the model's interpretability, providing insights into the contributions of individual sensors. Statistical analysis confirms the significant differences in sensor readings across different gas types. ExAIRFC-GSDC is a robust and explainable solution for accurate gas detection and classification in complex environments.https://doi.org/10.1007/s44196-025-00742-6Gas sensorsMachine learningExplainable artificial intelligenceStatistical analysis |
| spellingShingle | B. Lalithadevi S. Krishnaveni ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification International Journal of Computational Intelligence Systems Gas sensors Machine learning Explainable artificial intelligence Statistical analysis |
| title | ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification |
| title_full | ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification |
| title_fullStr | ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification |
| title_full_unstemmed | ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification |
| title_short | ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification |
| title_sort | exairfc gsdc an advanced machine learning based interpretable framework for accurate gas leakage detection and classification |
| topic | Gas sensors Machine learning Explainable artificial intelligence Statistical analysis |
| url | https://doi.org/10.1007/s44196-025-00742-6 |
| work_keys_str_mv | AT blalithadevi exairfcgsdcanadvancedmachinelearningbasedinterpretableframeworkforaccurategasleakagedetectionandclassification AT skrishnaveni exairfcgsdcanadvancedmachinelearningbasedinterpretableframeworkforaccurategasleakagedetectionandclassification |