Accurately measuring the recovery of released surface mines in the UnitedStates poses crucial challenges. This study aims to develop a prediction of land classification, that considers various environmental and coal mine variables. By utilizing this prediction, the researchers and environmentalists (specifically Appalachian Voices, the group heading this research) can better understand the relevant factors for successful reclamation. Efficient management of mine recovery is essential for environmental sustainability, regulatory compliance, and resource utilization. This study focuses on the Appalachian Forest area, which risks becoming a net carbon source (a place that emits more carbon than it absorbs) due to mine recovery. Machine and deep learning methods will be employed using Dynamic World land classification probabilities to identify areas requiring intervention and to provide ongoing insight into released mine conditions. The findings enable decision-making for prioritized reclamation and restoration measures.
Scott, Kendall; Webb, Austin; Backus, Tadd; and Slater, Robert
"Predicting Land Reclamation of Bond Released Surface Mines,"
SMU Data Science Review: Vol. 7:
3, Article 1.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss3/1
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