Development and Evaluation of Ensemble Learning-based Environmental
  Methane Detection and Intensity Prediction Models
Reek Majumder Jacquan Pollard M Sabbir Salek David Werth Gurcan Comert Adrian Gale Sakib Mahmud Khan Samuel Darko Mashrur Chowdhury
The environmental impacts of global warming driven by methane (CH4) emissions
have catalyzed significant research initiatives in developing novel
technologies that enable proactive and rapid detection of CH4. Several
data-driven machine learning (ML) models were tested to determine how well they
identified 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 the
simulation. We used the ensemble learning method to determine the
best-performing weighted ensemble ML models built upon several weaker
lower-layer ML models to (i) detect the presence of CH4 as a classification
problem and (ii) predict the intensity of CH4 as a regression problem.