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

Abstract

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. The classification model performance for CH₄ detection was evaluated using accuracy, F1 score, Matthew's Correlation Coefficient (MCC), and the area under the receiver operating characteristic curve (AUC ROC), with the top-performing model being 97.2%, 0.972, 0.945 and 0.995, respectively. The R² score was used to evaluate the regression model performance for CH₄ intensity prediction, with the R² score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH₄ detection and intensity prediction can be used with fixed environmental sensors deployed on the ground or with sensors mounted on unmanned aerial vehicles (UAVs) for mobile detection.