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Prediction of the groundwater quality index through machine learning in Western Middle Cheliff plain in North Algeria


Water quality monitoring and assessment has been one of the world’s major concerns in recent decades. This study examines the performance of three approaches based on the integration of machine learning and feature extraction techniques to improve water quality prediction in the Western Middle Cheliff plain in Algeria during 2014–2018. The most dominant Water Quality Index parameters that were extracted by neuro-sensitivity analysis (NSA) and principal component analysis (PCA) techniques were used in the multilayer perceptron neural network, support vector regression (SVR) and decision tree regression models. Various combinations of input data were studied and evaluated in terms of prediction performance, using statistical criteria and graphical comparisons. According to the results, the MLPNN1 model with eight input parameters gave the highest performance for both training and validation phases (R = 0.98/0.95, NSE = 0.96/0.88, RMSE = 11.20/15.03, MAE = 7.89/10.22 and GA = 1.34) when compared with the multiple linear regression, TDR and SVR models. Generally, the prediction performance of models integrated with NSA approaches is significantly improved and outperforms models coupled with the PCA dimensionality reduction method.

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Data availability

The data that support the findings of this study are available from the National Agency for Water Resources (ANRH) but restrictions apply on the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the author upon reasonable request and with permission of the National Agency for Water Resources (ANRH).


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The author acknowledges the support of the National Agency for Water Resources (ANRH) of Algeria, for providing the groundwater quality data of this study.


This research received no external funding.

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Authors and Affiliations



All authors contributed to the study conception. Data collection and analysis were performed by Yamina Elmeddahi. The first draft of the manuscript was written by Yamina Elmeddahi, Supervision by Ragab Ragab. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yamina Elmeddahi.

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The authors declare that they have no conflicting interests.

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Edited by Dr. Michael Nones (CO-EDITOR-IN-CHIEF).

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Elmeddahi, Y., Ragab, R. Prediction of the groundwater quality index through machine learning in Western Middle Cheliff plain in North Algeria. Acta Geophys. 70, 1797–1814 (2022).

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  • Water resources
  • Machine learning
  • SVR
  • M LP
  • DTR