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

• Main Authors: Reek Majumder (Author), Jacquan Pollard (Author), M Sabbir Salek (Author), David Werth (Author), Gurcan Comert (Author), Adrian Gale (Author), Sakib Mahmud Khan (Author), Samuel Darko (Author), Mashrur Chowdhury (Author)
• Format: Book
• Published: SAGE Publishing, 2024-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1178-6302; 10.1177/11786302241227307
• Keywords: Environmental sciences; GE1-350; Public aspects of medicine; RA1-1270
• Source: Environmental Health Insights, Vol 18 (2024)

The environmental impacts of global warming driven by methane (CH 4 ) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH 4 . Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH 4 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 CH 4 as a classification problem and (ii) predict the intensity of CH 4 as a regression problem. The classification model performance for CH 4 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 R 2 score was used to evaluate the regression model performance for CH 4 intensity prediction, with the R 2 score of the best-performing model being 0.858. The ML models developed in this study for fugitive CH 4 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.