A Multi-Sensor Classification Framework for Methane Leak Detection using Machine Learning Algorithms

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Enhanced leak detection improves safety and environmental monitoring effectiveness, addressing critical methane hazards.
The framework employs machine learning for classification and uses hybrid sensor fusion to increase detection accuracy.
Implementation follows a robust design using modular architecture and systematic analysis of deployment constraints.
Findings support the use of k-fold cross-validation to optimize performance in real-world detection scenarios.

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Proposed framework improves methane detection reliability in industrial settings, suggesting enhanced safety measures.

Fasasi et al. (2025) studied this question.

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