https://doi.org/10.7343/as-2025-804

Reassessment of Plio-Quaternary aquifer mineralization (Sidi Mansour plain, Southern Tunisia): a machine learning approach

Vol. 14 No. 1 (2025)
Submitted: 8 July 2024
Accepted: 6 February 2025
Published: 31 March 2025
Major elements, isotopes, mixing process, upward leakage, machine learning, salinity prediction
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Authors

Zohra Kraiem
zohra.kraiem@enis.tn
https://orcid.org/0000-0002-5676-6510
Radio-Analysis and Environment Laboratory, the National Engineering School, University of Sfax, Tunisia.
Kamal Zouari
Radio-Analysis and Environment Laboratory, the National Engineering School, University of Sfax, Tunisia.
Aissa Hleimi
Water Resources Directory, Ministry of Agriculture, Water Resources and Fishing, Tunisia.
Houda Derbel Fetoui
Faculty of Economic Sciences and Management of Nabeul, University of Carthage, MODILS Laboratory, Tunisia.

Understanding aquifer mineralization is crucial for sustainable water resource management. Thus, the Plio-Quaternary aquifer of the Sidi Mansour plain was studied in detail for reassessment of its heterogeneous mineralization water. Geochemical analysis and stable (18O, 2H) and radiogenic isotope (3H, 14C) analyses have provided a better understanding of the hydrodynamics and mineralization processes underlying the chemical composition. An artificial neural network was used for an integrated groundwater assessment in the Sidi Mansour area via the prediction of total dissolved solids (TDS). To the west, the Plio-Quaternary aquifer has a Na-Ca-SO4 facies with high salinity because of mineral dissolution, mixing, and upward percolation of deeper waters through the Om Ali fault (contribution up to 96%). To the east, predominantly Ca-SO4 waters are less mineralized due to the infiltration of recent precipitation. Salinity prediction using machine learning indicated that the proposed model achieved high efficiency. The backpropagation neural network model results (10:4:1) indicated high accuracy of the trained algorithm, as confirmed by a cross-validation test (high accuracy [88.89%], specificity [100%], and R2 [0.9687]). This study highlights the importance of this model in predicting salinity for the Plio-Quaternary aquifer in this region.

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How to Cite

Kraiem, Z., Zouari, K., Hleimi, A., & Derbel Fetoui, H. (2025). Reassessment of Plio-Quaternary aquifer mineralization (Sidi Mansour plain, Southern Tunisia): a machine learning approach. Acque Sotterranee - Italian Journal of Groundwater, 14(1). https://doi.org/10.7343/as-2025-804
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