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April 3, 2026Scientific ReportsOpen Access

Forecasting groundwater level changes using machine learning techniques in Tazerbo area, Al Kufra Basin, southeast Libya

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Authors

OFOsama A. El FallahLELobna M. Abou El-MagdMKMohamed M. El Kammar

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Overview

Model forecasts groundwater levels in Tazerbo, indicating trends for sustainable water management.

Key Points

  • The study aims to predict groundwater level changes using machine learning techniques in a water-stressed area.
  • Developed a NARX-NN model for predictions based on annual groundwater data from 2004 to 2024.
  • Collected data from 14 piezometric wells in Tazerbo, Al Kufra Basin.
  • Used statistical metrics (R2, MSE, RMSE) to train and validate the model.
  • Generated scenario-based forecasts for 2030 and 2040 under various pumping rates.
  • Forecasts predict a groundwater decline of approximately 2 m by 2030 and 1.6 m by 2040 at current pumping rates.
  • With higher extraction rates, water levels could drop by over 50 m by both 2030 and 2040.
  • Spatial analysis shows significant declines particularly in northern and eastern zones of the study area.

Cite This Study

Fallah et al. (2026) studied this question.

synapsesocial.com/papers/69cf5e2e5a333a821460c52ehttps://doi.org/10.1038/s41598-026-37337-w
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