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Development and Evaluation of Ensemble Learning-based Environmental Methane Detection and Intensity Prediction
Reek Majumder1, Jacquan Pollard1, M Sabbir Salek1
1Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA.
Environmental Health Insights
|February 29, 2024
Summary
Machine learning models accurately detect methane (CH4) leaks and predict their intensity. These advanced systems utilize meteorological data for effective fugitive CH4 monitoring.
Area of Science:
- Environmental Science
- Machine Learning
- Atmospheric Chemistry
Background:
- Global warming concerns necessitate advanced methane (CH4) detection technologies.
- Fugitive CH4 emissions pose significant environmental risks.
- Existing detection methods require enhancement for speed and accuracy.
Purpose of the Study:
- To develop and evaluate machine learning (ML) models for detecting fugitive methane (CH4).
- To predict the intensity of methane (CH4) emissions using ML.
- To integrate meteorological data for improved CH4 monitoring.
Main Methods:
- Utilized ensemble learning to build weighted ML models from weaker models.
- Trained models for CH4 detection (classification) and intensity prediction (regression).
- Incorporated meteorological variables: wind speed, temperature, pressure, humidity, water vapor, heat flux.
Main Results:
- Classification model achieved 97.2% accuracy, 0.972 F1 score, 0.945 MCC, and 0.995 AUC ROC for CH4 detection.
- Regression model achieved an R² score of 0.858 for predicting CH4 intensity.
- Ensemble ML approach demonstrated high efficacy in both detection and intensity prediction.
Conclusions:
- Developed ML models offer a robust solution for fugitive CH4 detection and intensity assessment.
- Models are adaptable for deployment with fixed ground sensors or mobile UAV-mounted systems.
- This technology can significantly aid in mitigating environmental impacts of CH4 emissions.
