Semester
Spring
Date of Graduation
2024
Document Type
Dissertation
Degree Type
PhD
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Xin Li
Committee Member
Shuo Wang
Committee Member
Matthew Valenti
Committee Member
Brian Powell
Committee Member
Natalia Schmid
Abstract
This research proposes a comprehensive approach to address pressing challenges in environmental sustainability, agricultural residue management, using machine learning based approaches. Machine learning (ML) techniques have emerged as powerful tools for addressing environmental sustainability challenges by facilitating the analysis and prediction of ecological phenomena, and optimization of resource management strategies. The study explores the synergies between environmental sustainability and machine learning to develop a framework that leverages artificial intelligence techniques covering a wide range of tasks including crop residue management, soil CO2 flux prediction, and forest carbon system prediction for sustainable development. The study analyze various ML models, such as, random forests, support vector machines, and ensemble learning techniques, highlighting their strengths and limitations. The contribution of this study not only enhances agricultural productivity but also mitigates environmental degradation associated with conventional farming practices. By synthesizing insights from environmental science, agriculture, and machine learning, this study not only contributes to the growing field of interdisciplinary research but also offers practical solutions to urgent global challenges at the intersection of sustainability and technology. Finally, we identify emerging trends and future research directions in this field, emphasizing the importance of interdisciplinary collaboration and the integration of domain expertise with ML methodologies to address complex environmental challenges effectively.
Recommended Citation
Ferdous, Syeda Nyma, "Machine Learning for Environmental Sustainability" (2024). Graduate Theses, Dissertations, and Problem Reports. 12374.
https://researchrepository.wvu.edu/etd/12374