FORECASTING GROUNDWATER BASIN DATA USING ARTIFICIAL INTELLIGENCE MODELS
DOI:
https://doi.org/10.69891/Keywords:
groundwater artificial intelligence forecasting hydrogeological data machine learning monitoring data analysisAbstract
This article thoroughly examines the issues of forecasting groundwater basin data using artificial intelligence models. The study investigates methods for analyzing groundwater data and identifying future changes based on hydrogeological and hydrodynamic indicators. The use of artificial intelligence models, particularly advanced architectures such as Transformer, LSTM, and GRU, makes it possible to identify complex relationships between hydrogeological processes and significantly improve the accuracy of forecasting results. These models are capable of accounting for both long-term and short-term dynamic changes, including seasonal cycles, anthropogenic impacts, and delayed chains of 'precipitation-runoff-water level-hydrochemical composition'. The research findings can be applied in groundwater resource monitoring, effective management of water resources, prevention of environmental problems, and the improvement of decision-making systems. Furthermore, the joint application of various artificial intelligence models and the integration of data from multiple sources is advisable in the future to enhance the reliability of forecasting results.References
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