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

Methane is a potent greenhouse gas and safety hazard, making its timely and accurate detection a critical objective in environmental monitoring and industrial safety. Traditional methane detection systems, often reliant on single-sensor architectures or manual inspections, suffer from limited coverage, high false alarm rates, and poor adaptability to dynamic conditions. This paper proposes a multi-sensor classification framework that integrates heterogeneous sensing technologies with machine learning algorithms to enhance leak detection reliability. The framework incorporates a hybrid sensor fusion strategy, robust feature extraction, and interpretable classification models such as support vector machines and ensemble methods. By addressing challenges related to sensor drift, environmental noise, and data redundancy, the system ensures scalable, fault-tolerant, and explainable detection. Theoretical considerations guide the design of a modular architecture capable of handling synchronized, normalized sensor data across diverse conditions. Practical implementation factors, including deployment constraints, fusion logic, and decision support mechanisms, are systematically analyzed. Evaluation protocols based on k-fold cross-validation and post hoc interpretability techniques ensure the framework meets the performance and transparency requirements of real-world applications. This work contributes a scalable, intelligent solution to methane leak detection and lays the groundwork for future advancements in adaptive learning, anomaly detection, and edge-based sensing systems.