Empress Catherine II Saint-Petersburg Mining University (Saint-Petersburg, Russia)

D. L. Ustyugov, Dean, Candidate of Geological and Mineralogical Sciences, Associate Professor, Ustyugov_DL@pers.spmi.ru

Empress Catherine II Saint-Petersburg Mining University (Saint-Petersburg, Russia)¹ ; University of Ciego de Avila Maximo Gomez Baez (Havana, Cuba)²

H. L. Noa Segura, Post-Graduate¹, Master’s Student²

Institute of Geology and Paleontology—Geological Service of Cuba (Havana, Cuba)

Y. C. Mila-Doma, Master’s Student

In view of climate change, it is anticipated that many mines will be faced with a growing deficit of water in the nearest decades. This defines the relevance of studying and evaluating quantity and quality of groundwater. Considering that mineral mining exerts great effects on vast areas and aquifers, the environmental and cartographic documentation was created in the field of environmental protection and groundwater dynamics control as a case-study of metallic ore deposits in the Republic of Cuba. The methods were HGBR machine-learning algorithm and CMIP6 climate scenarios. The monthly groundwater levels obtained in 45 observation wells over the period from 1986 to 2024 were provided by Cuba’s National Institute of Hydraulic Resources (NIHR). The climate predicators were the total monthly rainfalls and evapotranspiration values from NEX-GDDP-CMIP6 dataset. The analysis revealed deficient waterspreading and confirmed depletion of aquifers at the current level of groundwater supply. The study proves efficiency of the proposed procedure which integrates HGBR-based machine-learning models with decomposition in time and climate projections from CMIP6 dataset, allowing high-precision prediction of groundwater dynamics in the condition of nonstationarity and statistical nonuniformity. The predicted changes in groundwater levels demonstrate a growing coefficient of variability after 2030, thus, it is urgently required to implement groundwater control within the framework of the mining project in the study area.

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