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| dc.contributor.author | Parvathareddy, Sravani | |
| dc.contributor.author | Yahya, Abid | |
| dc.contributor.author | Amuhaya, Lilian Livutse | |
| dc.contributor.author | Ravi, Samikannu | |
| dc.date.accessioned | 2026-07-07T10:13:15Z | |
| dc.date.available | 2026-07-07T10:13:15Z | |
| dc.date.issued | 2024-07-22 | |
| dc.identifier.citation | Parvathareddy, S., Yahya, A., Amuhaya, L. L., & Ravi, S. (2024). Hybrid machine learning and genetic algorithms for environmental impact and energy management to enhance mining sustainability. In Proceedings of the US-Botswana Workshop on Research Technologies in Water and Energy Needs for Remote, Austere Locations 2024 (WORTHWEEDS 2024), 22 - 25 July 2024, Maun, Botswana International University of Science and Technology, 115-120. | en_US |
| dc.identifier.issn | 2521-2294 | |
| dc.identifier.uri | https://repository.biust.ac.bw/handle/123456789/748 | |
| dc.description.abstract | The mining industry confronts the challenge of balancing economic prosperity and environmental sustainability. This research presents a comprehensive approach that leverages big data analytics and machine learning techniques to address this challenge. Unlike traditional approaches that focus on mitigating impacts post-occurrence, our method advocates for proactive measures throughout operational phases. We introduce a cohesive system integrating advanced technologies to analyze vast datasets, including real-time environmental sensor data, satellite imagery, company reports, and government records. The framework encompasses data pre-processing, model building, analysis, and recommendations. To predict environmental outcomes and assess sustainability, we employ a genetic algorithm (GA) and machine learning tools such as XGBoost, support vector regressor (SVR), and K-nearest neighbors (KNN) regressor algorithms. Data pre-processing ensures data accuracy and consistency. We use clustering and recommendation algorithms for analysis and suggestions, identify improvement areas, and propose environmental management solutions. This methodology underscores the importance of empowering stakeholders to anticipate environmental consequences, mitigate potential hazards, and continuously improve sustainability initiatives through real-time insights. By integrating big data analytics and machine learning, we enhance the environmental sustainability of mining operations, fostering a harmonious balance between environmental stewardship and economic returns. The benefits of our system are manifold, including improved environmental management, reduced environmental risks, and enhanced sustainability practices in the mining industry, thereby highlighting the crucial role of stakeholders in this process. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Botswana International University of Science and Technology | en_US |
| dc.subject | Mining industry | en_US |
| dc.subject | Environmental sustainability | en_US |
| dc.subject | Big data analytics | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Integrated framework | en_US |
| dc.subject | Proactive measures | en_US |
| dc.subject | Environmental impact assessment | en_US |
| dc.subject | Sustainability practices | en_US |
| dc.subject | Data-driven approaches | en_US |
| dc.subject | Predictive modeling | en_US |
| dc.title | Hybrid machine learning and genetic algorithms for environmental impact and energy management to enhance mining sustainability | en_US |
| dc.description.level | phd | en_US |
| dc.description.accessibility | unrestricted | en_US |
| dc.description.department | cte | en_US |
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