Date of Award


Document Type


Degree Name

Master of Science (MS)


Civil Engineering

Committee Chair/Advisor

Dr. Mashrur Chowdhury

Committee Member

Dr. Sakib Khan

Committee Member

Dr. Bryan Riley


The environmental impacts of global warming driven by fugitive methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4 emissions. This study evaluated the performance of data-driven machine learning (ML) models using support vector machines (SVM) to detect the presence of trace CH4 emissions and the corresponding intensity amongst various meteorological conditions. The author used simulation data comprising various meteorological parameters such as temperature, relative humidity, wind speed, water vapor, pressure, precipitation rate, and a parameter possessing trace concentrations of CH4 emissions. The novelty of the SVM models developed in this work lies in the ability to (i) detect the presence of trace concentrations of CH4 as a classification task, and (ii) predict the corresponding intensity of CH4 levels using AI-based regression analysis. The metrics used to assess the classification performance for SVM CH4 detection were accuracy, F1-score, and area under the receiver operating characteristic curve (AUC-ROC) with results for the best-performing model being 0.89, 0.89, and 0.94, respectively. On the other hand, the root mean squared error (RMSE) and mean absolute percentage error (MAPE) scores were used to evaluate the regression model performance for trace CH4 intensity prediction, achieving scores of 0.98 and 1.22 respectively, thereby demonstrating the reliable low error probability of the regression model in forecasting levels of trace CH4 emissions.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.