August 4, 2025

Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria

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Authors

LNLoubna Nefla ABAmira BergalUniversity of Guelma WBWarda BOUMARAF

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Implication

This research demonstrates the application of machine learning and statistical analysis to evaluate groundwater quality in a semi-arid aquifer, suggesting sustainable irrigation management practices.

Key Points

Groundwater quality assessment in northeastern Algeria revealed moderate to high constraints for irrigation use.
The Random Forest model outperformed other machine learning algorithms, ensuring accurate predictions of water quality indices.
Seasonal variations indicated that summer samples showed poorer groundwater quality due to evaporative processes.
Findings emphasize the need for effective management strategies to maintain sustainable irrigation practices in vulnerable regions.

Cite This Study

Nefla et al. (2025) studied this question.

synapsesocial.com/papers/689a0f8de6551bb0af8d0cd0 https://doi.org/10.21203/rs.3.rs-7051963/v1

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Implication

This research demonstrates the application of machine learning and statistical analysis to evaluate groundwater quality in a semi-arid aquifer, suggesting sustainable irrigation management practices.

Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria

Discussion

Member takes

Implication

Key Points

Cite This Study

Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria

Discussion

Member takes

Implication

Key Points

Cite This Study

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