ArticleScientific reports2026
Forecasting groundwater level changes using machine learning techniques in Tazerbo area, Al Kufra Basin, southeast Libya.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Due to the increasing demand for consumable water, groundwater management is critical, especially in arid and semi-arid regions. Effective management strategies are crucial for maintaining a sustainable water supply and promoting environmental health. Machine learning models can identify patterns and trends that help forecast fluctuations in groundwater levels. These predictions are essential for sustainable water resource management, thereby preventing water shortages. In this study, a machine learning model based on a time series neural network "Nonlinear Autoregressive Exogenous Neural Network (NARX-NN)" was made to predict the groundwater levels in Tazerbo, Al Kufra Basin, southeast Libya. The proposed model uses annual data of the groundwater levels for the last two decades (2004-2024) collected from 14 piezometric wells in the study area. The model was trained and validated using statistical performance metrics, including R
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