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Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment

Author

Listed:
  • Anna Kopeć

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Paweł Trybała

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Dariusz Głąbicki

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Anna Buczyńska

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Karolina Owczarz

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Natalia Bugajska

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Patrycja Kozińska

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Monika Chojwa

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

  • Agata Gattner

    (Department of Mining and Geodesy, Faculty of Geoengineering, Mining and Geology, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland)

Abstract

Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the statistical significance of the selected factors’ individual impact on the occurrence of wetlands. Obtained results show that Normalized Difference Vegetation Index (NDVI) change, terrain height, groundwater level and terrain displacement had a considerable influence on the occurrence of wetlands in the research area. Moreover, the machine learning model developed using the Random Forest algorithm allowed for an efficient determination of potential flooding zones based on a set of spatial variables, correctly detecting 76% area of wetlands. Finally, the GWR (Geographically Weighted Regression (GWR) modelling enabled identification of local anomalies of selected factors’ influence on the occurrence of wetlands, which in turn helped to understand the causes of wetland formation.

Suggested Citation

  • Anna Kopeć & Paweł Trybała & Dariusz Głąbicki & Anna Buczyńska & Karolina Owczarz & Natalia Bugajska & Patrycja Kozińska & Monika Chojwa & Agata Gattner, 2020. "Application of Remote Sensing, GIS and Machine Learning with Geographically Weighted Regression in Assessing the Impact of Hard Coal Mining on the Natural Environment," Sustainability, MDPI, vol. 12(22), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:22:p:9338-:d:442806
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    References listed on IDEAS

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    1. Andrzej Kwinta & Robert Gradka, 2018. "Mining exploitation influence range," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 979-997, December.
    2. Lechner, Alex Mark & Kassulke, Owen & Unger, Corinne, 2016. "Spatial assessment of open cut coal mining progressive rehabilitation to support the monitoring of rehabilitation liabilities," Resources Policy, Elsevier, vol. 50(C), pages 234-243.
    3. Jan Blachowski & Anna Kopeć & Wojciech Milczarek & Karolina Owczarz, 2019. "Evolution of Secondary Deformations Captured by Satellite Radar Interferometry: Case Study of an Abandoned Coal Basin in SW Poland," Sustainability, MDPI, vol. 11(3), pages 1-21, February.
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    Cited by:

    1. Maciej Dwornik & Justyna Bała & Anna Franczyk, 2021. "Application of a New Semi-Automatic Algorithm for the Detection of Subsidence Areas in SAR Images on the Example of the Upper Silesian Coal Basin," Energies, MDPI, vol. 14(11), pages 1-14, May.
    2. Ming Li & Yueguan Yan & Huayang Dai & Zhaojiang Zhang, 2023. "Study on Rock and Surface Subsidence Laws of Super-High Water Material Backfilling and Mining Technology: A Case Study in Hengjian Coal Mine," Sustainability, MDPI, vol. 15(11), pages 1-22, May.
    3. Shaokun He & Lei Gu & Jing Tian & Lele Deng & Jiabo Yin & Zhen Liao & Ziyue Zeng & Youjiang Shen & Yu Hui, 2021. "Machine Learning Improvement of Streamflow Simulation by Utilizing Remote Sensing Data and Potential Application in Guiding Reservoir Operation," Sustainability, MDPI, vol. 13(7), pages 1-15, March.

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