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The environmental impacts of global warming driven by methane (CH4) emissionshave catalyzed significant research initiatives in developing noveltechnologies that enable proactive and rapid detection of CH4. Severaldata-driven machine learning (ML) models were tested to determine how well theyidentified fugitive CH4 and its related intensity in the affected areas.Various meteorological characteristics, including wind speed, temperature,pressure, relative humidity, water vapor, and heat flux, were included in thesimulation. We used the ensemble learning method to determine thebest-performing weighted ensemble ML models built upon several weakerlower-layer ML models to (i) detect the presence of CH4 as a classificationproblem and (ii) predict the intensity of CH4 as a regression problem.

Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction Models