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The Digital Platform for Resolving Problems of Ecology of Disturbed Lands for Mining Companies with Open-pit Mining Based on Remote Sensing Resources and Artificial Intelligence

https://doi.org/10.18412/1816-0395-2024-1-52-57

Abstract

A justification for the creation of a center for remote sensing of the Earth from space is presented in order to monitor the environmental condition of the companies producing solid minerals on a federal scale. The use of artificial intelligence elements in the search for environmentally acceptable geometric parameters of the refuse dumps is shown. The structure of an algorithm for substantiating the technologies for formation and reclamation of the refuse dumps is disclosed, considering the natural and climatic characteristics of the territories where the mineral deposits are being developed. The interaction of information flows with subjects in the ecology of the mining industry is detailed.

About the Authors

I.V. Zenkov
Siberian Research Institute of Mining and Surveying
Russian Federation

Dr. Sci. (Eng.), Deputy Director for Research



E.A. Kustikova
Siberian Research Institute of Mining and Surveying
Russian Federation

Engineer-elocogist



Chin Le Hung
Le Quy Don Technical University
Viet Nam

Cand. Sci. (Eng.), Associate Professor



O.V. Silvanovich
Saint Petersburg Mining University
Russian Federation

Cand. Sci. (Economics), Associate Professor



Yu.P. Yuronen
Reshetnev Siberian State University of Science and Technology
Russian Federation

Cand. Sci. (Eng.), Associate Professor



Yu.A. Maglinets
Siberian Federal University
Russian Federation

Cand. Sci. (Eng.), Professor



K.V. Raevich
Siberian Federal University
Russian Federation

Cand. Sci. (Eng.), Senior lecturer



E.I. Gerasimova
Siberian Federal University
Russian Federation

Senior Lecturer



Zh.V. Mironova
Siberian Federal University
Russian Federation

Cand. Sci. (Eng.), Associate Professor



S.N. Skornyakova
Siberian Federal University
Russian Federation

Senior Lecturer



References

1. Горный В.И., Бровкина О.В., Киселев А.В., Тронин А.А. Тенденции развития дистанционных методов при решении задач геологии и экологической безопасности. Современные проблемы дистанционного зондирования Земли из космоса. 2023. Т. 20. № 2. С. 9—38.

2. Скороходов А.В., Курьянович К.В. Использование данных CloudSat CPR для повышения эффективности нейросетевого подхода к восстановлению высоты нижней границы облаков на спутниковых снимках Aqua MODIS. Современные проблемы дистанционного зондирования Земли из космоса. 2022. Т. 19. № 5. С. 63—75.

3. Коротаева А.Э., Пашкевич М.А. Применение данных спектральной съемки для экологического мониторинга водной растительности. Горный информационно-аналитический бюллетень. 2021. № 5—2. С. 231—244.

4. Singh P., Pani A., Mujumdar A.S., Shirkole Sh.S. New strategies on the application of artificial intelligence in the field of phytoremediation. International Journal of Phytoremediation. 2023. Vol. 25. Iss. 4. P. 505—523.

5. Gautam K., Sharma P., Dwivedi Sh. et al. A review on control and abatement of soil pollution by heavy metals: Emphasis on artificial intelligence in recovery of contaminated soil. Environmental Research. 2023. Vol. 225. 115592.

6. Hadid N.B., Goyet C., Chaar H. et al. Machine Learning Modeling Techniques for Forecasting the Trophic Level in a Restored South Mediterranean Lagoon Using Chlorophyll-a. Wetlands. 2021. Vol. 41. 111.

7. Cai P., Chen G., Yang H. et al. Detecting Individual Plants Infected with Pine Wilt Disease Using Drones and Satellite Imagery: A Case Study in Xianning, China. Remote Sensing. 2023. Vol. 15(10). 2671. https://doi.org/10.3390/rs15102671.

8. Li J., Liu H., Du J. et al. Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm. Remote Sensing. 2023. Vol. 15(10). 2641. https://doi.org/10.3390/rs15102641.

9. Chen T., Tang G., Yuan Y. et al. Disentangling the Key Drivers of Ecosystem Water-Use Efficiency in China’s Subtropical Forests Using an Improved Remote-Sensing-Driven Analytical Model. Remote Sens. 2023. Vol. 15. 2441. https://doi.org/10.3390/rs15092441.

10. McGovern A., Ebert-Uphoff I., Gagne D., Bostrom A. Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science. Environmental Data Science. 2022. Vol. 1. E6. doi:10.1017/eds.2022.5.

11. Wang W., Wan S., Xiao P., Zhang X. A Novel Multi- Training Method for Time-Series Urban Green Cover Recognition From Multitemporal Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022. Vol. 15. P. 9531—9544. doi: 10.1109/JSTARS.2022.3218919.

12. Du B., Mao D., Wang Z. et al. Mapping Wetland Plant Communities Using Unmanned Aerial Vehicle Hyperspectral Imagery by Comparing Object/Pixel-Based Classifications Combining Multiple Machine-Learning Algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021. Vol. 14. P. 8249—8258. doi: 10.1109/JSTARS.2021.3100923.


Review

For citations:


Zenkov I., Kustikova E., Le Hung Ch., Silvanovich O., Yuronen Yu., Maglinets Yu., Raevich K., Gerasimova E., Mironova Zh., Skornyakova S. The Digital Platform for Resolving Problems of Ecology of Disturbed Lands for Mining Companies with Open-pit Mining Based on Remote Sensing Resources and Artificial Intelligence. Ecology and Industry of Russia. 2024;28(1):52-57. (In Russ.) https://doi.org/10.18412/1816-0395-2024-1-52-57

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ISSN 1816-0395 (Print)
ISSN 2413-6042 (Online)