Master of Science, The Ohio State University, 2024, Food, Agricultural and Biological Engineering

Waste coal in abandoned mine lands poses significant environmental challenges, affecting nearby communities, rivers, and streams. Effective management of these piles is essential due to concerns such as acid mine drainage, soil and water contamination, coal fires, and methane emissions. Various strategies have been proposed for managing waste coal, including potential utilization for rare earth element recovery, soil amendment, construction aggregates, and energy generation. However, the implementation of these strategies remains uncertain due to the lack of precise location and volume data on waste coal piles. Traditional methods for gathering these data rely on field visits and Global Navigation Satellite System surveying, which are costly and labor-intensive. Advances in satellite technologies and the availability of digital elevation models (DEMs) offer an opportunity to estimate waste coal volume on a regional scale in a timely and cost-effective manner. Thus, the objective of this thesis was to develop a robust data analytical framework to locate and estimate the volume of waste coal piles on a regional scale, using the Muskingum River Basin (MRB) in Ohio as the study area. Initially, a prototype was developed to determine the most effective machine learning (ML) model to map waste coal piles in a historical coal mine site within the MRB. While all four ML models effectively identified dominant classes such as Grassland and Forest, the Random Forest (RF) model demonstrated superior performance in classifying the more complex waste coal class, with a precision of 86.15% and recall of 76.71%. Subsequently, the greatest disturbance and reclamation mapping of these waste coal piles were conducted using the LandTrendr algorithm to distinguish waste coal piles in abandoned mine lands from those in active mining areas. Moreover, this study utilized publicly available elevation models to estimate waste coal volume in the MRB. However, since historical terrain mo (open full item for complete abstract)

Committee: Ajay Shah (Advisor); Sami Khanal (Advisor); Tarunjit Singh Butalia (Committee Member) Subjects: Artificial Intelligence; Engineering; Geographic Information Science; Remote Sensing; Sustainability

Master of Science in Electrical Engineering, University of Dayton, 2024, Electrical and Computer Engineering

Dynamic Thermal Point Cloud (DTPC) technology represents a cutting-edge approach in the field of remote sensing, offering unprecedented capabilities for monitoring volumetric changes in objects, analyzing structural integrity in existing constructions, and conducting comprehensive thermal audits. This innovative methodology integrates high-resolution thermal imaging with advanced point cloud data analytics, allowing for the precise capture and analysis of thermal properties across various surfaces and structures in real time. DTPC leverages the synergy between thermal imaging technology and 3D scanning to generate detailed, multi-dimensional representations of objects and environments. These representations are not only accurate but also thermal information, enabling the detection of temperature variations with high precision. DTPC offers a robust tool for assessing the structural integrity and thermal efficiency of buildings and infrastructure. By providing a detailed view of thermal anomalies, such as heat leaks or insulation failures, it facilitates targeted interventions that enhance energy efficiency. Furthermore, DTPC stands out by offering a comprehensive and non-invasive means of evaluating the thermal performance of facilities and systems. Through its detailed thermal and spatial analysis, it identifies areas of energy loss, inefficient HVAC performance, and other factors contributing to increased energy consumption. This information is invaluable for implementing corrective measures that optimize energy usage, reduce operational costs, and contribute to environmental sustainability.

Committee: Bradley Ratliff (Committee Member); Keigo Hirakawa (Committee Member); Hui Wang (Committee Chair) Subjects: Electrical Engineering