| Библиографический список |
1. Cuthbert M. O., Gleeson T., Bierkens M. F. P., Ferguson G., Taylor R. G. Defining Renewable Groundwater Use and Its Relevance to Sustainable Groundwater Management // Water Resources Research. 2023. Vol. 59. No. 9. DOI: 10.1029/2022WR032831 2. Головина Е. И., Чиркина С. А. Оптимизация системы налогообложения добычи подземных вод в Российской Федерации // Геология и минерально-сырьевые ресурсы Сибири. 2025. № 4-2(64). С. 185–194. 3. Barua S., Cartwright I., Dresel P. E., Daly E. Using multiple methods to investigate the effects of land-use changes on groundwater recharge in a semi-arid area // Hydrology and Earth System Sciences. 2021. Vol. 25. No. 1. С. 89–104. 4. Головина Е. И., Гребнева А. В. Управление ресурсами подземных вод на трансграничных территориях (на примере Российской Федерации и Эстонской Республики) // Записки Горного института. 2021. Т. 252. С. 788–800. 5. Golovina E., Grebneva A. Some Aspects of Groundwater Resources Management in Transboundary Areas // Journal of Ecological Engineering. 2021. Vol. 22. No 4. P. 106–118. 6. Yi Z., Cao J., Jiang T., Wang Z. Characterization of metal-bearing particles in groundwater from the Weilasituo Zn-Cu-Ag deposit, Inner Mongolia, China: Implications for mineral exploration // Ore Geology Reviews. 2020. Vol. 117. ID 103270. 7. Meißner S. The Impact of Metal Mining on Global Water Stress and Regional Carrying Capacities–A GIS-Based Water Impact Assessment // Resources. 2021. Vol. 10. No. 12. ID 120. 8. Таловина И. В., Бабенко И. А., Илалова Р. К., Дурягина А. М. Оливиншпинелевая геотермометрия – индикатор формационной принадлежности пород и основа для геодинамических реконструкций в условиях Антарктиды // Горный журнал. 2024. № 9. С. 77–82.
9. Hilili J. M., Onuora D. I., Hilili R. U., Annah A. F., Onmonya Y. A. et al. Ground Water Contamination: Effects and Remedies // Asian Journal of Environment & Ecology. 2021. Vol. 14. No. 1. P. 39–58. 10. Minnaar A. Water Pollution and Contamination from Gold Mines: Acid Mine Drainage in Gauteng Province, South Africa // Water, Governance, and Crime Issues. – Cham : Springer, 2020. P. 193–219. 11. Chen Z., Yang Y., Zhou L., Hou H., Zhang Y. et al. Ecological restoration in mining areas in the context of the Belt and Road initiative: Capability and challenges // Environmental Impact Assessment Review. 2022. Vol. 95. ID 106767. 12. Ezhilarasi M., Senthilkumar S., Senthilkumar V., Sampathkumar V. Groundwater hydrochemistry and its appropriateness for consumption and irrigation: Geographic and temporal variation: Integrated approach // Urban Climate. 2023. Vol. 49. ID 101482. 13. Amarán N. C. B., Duque J. A. D., Santander J. R. Á., Díaz R. H., Hernández R. R. et al. Impacto de los pasivos ambientales en la red hidrográfica de la región minera de Santa Lucía, Minas de Matahambre, Cuba // Ingeniería Hidráulica y Ambiental. 2022. Vol. 43. No. 1. P. 63–78. 14. Шклярский Я. Э., Герра Д. Д., Яковлева Э. В., Рассылкин А. Влияние солнечной энергетики на развитие горнодобывающей отрасли в Республике Куба // Записки Горного института. 2021. Т. 249. С. 427–440. 15. Toujaguez R., Ono F. B., Martins V., Cabrera P. P., Blanco A. V. et al. Arsenic bioaccessibility in gold mine tailings of Delita, Cuba // Journal of Hazardous Materials. 2013. Vol. 262. P. 1004–1013. 16. González H., Ramírez M. The effect of nickel mining and metallurgical activities on the distribution of heavy metals in Levisa Bay, Cuba // Journal of Geochemical Exploration. 1995. Vol. 52. No. 1-2. P. 183–192. 17. Zhu G., Wu X., Ge J., Liu F., Zhao W. et al. Influence of mining activities on groundwater hydrochemistry and heavy metal migration using a selforganizing map (SOM) // Journal of Cleaner Production. 2020. Vol. 257. ID 120664. 18. Ewusi A., Tetteh S. E. K., Seidu J., Ahenkorah I. Hydrogeological risk assessment for mineral exploration in Ghana: A brief overview // Scientific African. 2024. Vol. 24. ID e02218. 19. Ewusi A., Ahenkorah I., Kuma J. S. Y. Groundwater Vulnerability Assessment of the Tarkwa Mining Area Using SINTACS Approach and GIS // Ghana Mining Journal. 2017. Vol. 17. No. 1. P. 18–30. 20. Molerio-León L. F., Sardiñas Gómez A. M. Evaluación de los recursos hídricos de Cuba (II): dominio de los acuíferos cársicos, regionalización y recursos potenciales de agua subterránea // Gota a Gota. 2023. No. 29. P. 33–46. 21. Molerio-León L. Evaluación de los recursos hídricos de Cuba (I): aguas subterráneas en el karst de montaña: métodos de cálculo // Gota a Gota. 2023. No. 27. P. 69–78. 22. Gutiérrez E. O. P. Aproximación a los recursos hídricos potenciales en Cuba al 2030 // Ingeniería Hidráulica y Ambiental. 2022. Vol. 43. No. 1. P. 48–62. 23. Vichot-Llano A., Bezanilla-Morlot A., Martínez-Castro D. Estado actual de la aplicación de métodos de reducción de escala a las proyecciones de cambio climático en Centroamérica y el Caribe // Revista Cubana de Meteorología. 2019. Vol. 25. No. 2. P. 218–237. 24. Silva J. L. B. Evaluación de los recursos hídricos de Cuba // Revista Geográfica. 2016. No. 157. P. 73–84. 25. Galevskiy S. G., Qian H. Developing and validating comprehensive indicators to evaluate the economic efficiency of hydrogen energy investments // Operational Research in Engineering Sciences: Theory and Applications. 2024. Vol. 7. Iss. 3. P. 188–207. 26. Tang L., Pervukhin D. A. Enhancing operational efficiency in coal enterprises through capacity layout optimisation: a cost-effectiveness analysis // Operational Research in Engineering Sciences: Theory and Applications. 2024. Vol. 7. Iss. 3. P. 144–163. 27. Lange H., Sippel S. Machine Learning Applications in Hydrology // Forest-Water Interactions. Series: Ecological Studies. – Cham : Springer, 2020. Vol. 240. P. 233–257. 28. Sharghi A., Komasi M., Ahmadi M. Variable sensitivity analysis in groundwater level projections under climate change adopting a hybrid machine learning algorithm // Environmental Modelling & Software. 2025. Vol. 183. ID 106264. 29. Kottek M., Grieser J., Beck C., Rudolf B., Rubel F. World Map of the Köppen-Geiger climate classification updated // Meteorologische Zeitschrift. 2006. Vol. 15. No. 3. P. 259–263. 30. Hernández Víctor Y., Almeida Maldonado E., Brown Manrique O. Uso de minería de datos para la desestacionalización de série de datos de precipitación en el municipio de Venezuela // Revista Cubana de Ciencias Informáticas. 2023. Vol. 17. No. 2. P. 21–35. 31. Yamazaki D., I keshima D., Tawatari R., Yamaguchi T., O’Loughlin F. et al. A highaccuracy map of global terrain elevations // Geophysical Research Letters. 2017. Vol. 44. No. 11. P. 5844–5853. 32. Olivera V. M. V., Fernández R. G.-A., Peña Y. J., González L. A. V., Carrillo M. C. et al. Vulnerabilidad a la contaminación del acuífero norte de la provincia Ciego de Ávila // Ingeniería Hidráulica y Ambiental. 2015. Vol. 36. No. 2. P. 45–56. 33. Anuario Estadístico de Cuba 2023. Capítulo 1: Territorio. Edición 2024. – La Habana : Oficina Nacional de Estadística e Informcion, 2024. – 23 p. 34. Cala E. L., Delgado D. E. G. Stratigraphy of Cuba // Geology of Cuba. Series: Regional Geology Reviews. – Cham : Springer, 2021. P. 143–188. 35. Salgado E. J. J., Oliva M. G., Durán B. P. Caracterización y mapeo a escala grande del karst superficial de la provincia de Ciego de Ávila, Cuba // Gota a Gota. 2024. No. 32. P. 41–47. 36. De Kirchner C. F., Fernández A., Lorusso S., Vismara J. P. Tercera Comunicación Nacional de la Republica Argentina a la Convencion Marco de las Naciones Unidas Sobre el Cambio Climatico. – La Habana : Secretaría de Ambiente y Desarrollo Sustentable de la Nación, 2020. – 264 p. 37. Thrasher B., Wang W., Michaelis A., Melton F., Lee T. et al. NASA Global Daily Downscaled Projections, CMIP6 // Scientific Data. 2022. Vol. 9. DOI: 10.1038/s41597-022-01393-4 38. Meinshausen M., Nicholls Z. R. J., Lewis J., Gidden M. J., Voge E. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500 // Geoscientific Model Development. 2020. Vol. 13. No. 8. P. 3571– 3605. 39. Червяков А. А., Никульчев Е. В. Робастное интервальное прогнозирование временных рядов // International Journal of Open Information Technologies. 2023. Т. 11. № 4. С. 122–128. 40. Жерлыгина Е. С ., Куранова М. Е., Гусев В. Н., Одинцов В. Н. Выявление опасных участков на основе исследования развития техногенных трещин в толще слагающих массив пород // Горная промышленность. 2025. № 1. С. 162–169. 41. Гусев В. Н., Одинцов Е. Е., Жерлыгина Е. С. Расчет сдвижений и деформаций массива горных пород с учетом натурных данных // Горный журнал. 2025. № 4. С. 18–25. 42. Dokumentov A., Hyndman R. J. STR: Seasonal-Trend decompos ition using Regression // INFORMS Journal on Data Science. 2022. Vol. 1. No. 1. P. 50–62. 43. Khorrami B., Gunduz O. An enhanced water storage deficit index (EWSDI) for drought detection using GRACE gravity estimates // Journal of Hydrology. 2021. Vol. 603. ID 126812. 44. Satizábal-Alarcón D. A., Suhogusoff A., Ferrari L. C. Characterization of groundwater storage changes in the Amazon River Basin based on downscaling of GRACE/GRACE-FO data with machine learning models // Science of The Total Environment. 2024. Vol. 912. ID 168958. 45. Danilov A., Serdiukova E. Review of Methods for Automatic Plastic Detection in Water Areas Using Satellite Images and Machine Learning // Sensors. 2024. Vol. 24. Iss. 16. ID 5089. 46. Bojer A. K., Biru B. H., Al-Quraishi A. M. F., Debelee T. G., Negera W. G. et al. Machine learning and remote sensing based time series analysis for drought risk prediction in Borena Zone, Southwest Ethiopia // Journal of Arid Environments. 2024. Vol. 222. ID 105160.
47. Dongliang M., Tao Z., Yanping H. Optimization research of heat transfer coefficient prediction model for supercritical water based on Bayesian search algorithm // Nuclear Engineering and Design. 2025. Vol. 438. ID 114036. 48. Nhat-Duc H., Van-Duc T. Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification // Automation in Construction. 2023. Vol. 148. ID 104767. 49. Asrade T. M. Application of machine learning in python for temporal groundwater level prediction // Solid Earth Sciences. 2025. Vol. 10. Iss. 3. ID 100261. 50. Lundberg S. M., Erion G., Chen H., DeGrave A., Prutkin J. M. et al. From local explanations to global understanding with explainable AI for trees // Nature Machine Intelligence. 2020. Vol. 2. No. 1. P. 56–67. 51. Dahm Z., Theos V., Vasili K., Richards W., Gkouliaras K. et al. A one-class explainable AI framework for identification of non-stationary concurrent false data injections in nuclear reactor signals // Nuclear Engineering and Design. 2025. Vol. 444. ID 114359. 52. Бабенко И. А., Таловина И. В., Ушаков Д. Е., Крикун Н. С. Пегматиты оазиса Холмы Ларсеманн, Восточная Антарктида: новые полевые геологические и геофизические данные // Записки Горного института. 2025. Т. 273. С. 65–79. 53. Liu Q., Gui D., Zhang L., Niu J., Dai H. et al. Simulation of regional groundwater levels in arid regions using interpretable machine learning models // Science of The Total Environment. 2022. Vol. 831. ID 154902. 54. Adombi A. V. D. P., Chesnaux R., Boucher M.-A., Braun M., Lavoie J. A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis // Journal of Hydrology. 2024. Vol. 637. ID 131370. |