Citation:
Abstract:
Groundwater from coastal aquifers plays a significant role in agriculture, but its diminishing of quality often impacts crop production and soil sustainability by leading to soil salinization and the deterioration of irrigation water standards. This study addresses the pressing issue at Mornang Plain in Tunisia utilizing an integrated approach that combines statistical analysis (principal component analysis (PCA) and cluster analysis (CA), geographic information system (GIS), and machine learning (ML) techniques to assess and predict irrigation water quality. Key parameters such as irrigation water quality index (IWQI), potential salinity (PS), sodium percentage (Na%), and sodium adsorption ratio (SAR) were evaluated to assess water quality for agricultural use. The study identified three main groundwater facies (Na-Cl, Ca-Mg-SO4, Ca-Mg-Cl/SO4), that displaying distinct chemical signatures shaped by geological, hydrological, and human processes. The analysis showed that over 65% of the groundwater samples fall within the “unsuitable” category for irrigation, with high to severe constraints for soil and crop sustainability. A novel decision tree (DT) based ML model was optimized to predict these irrigation indices, achieving high performance with fewer input parameters. With low RMSE values and R2 values ranging from 0.706 to 0.996 across several indices, the DT models showed remarkable predictive accuracy. The models’ efficiency in producing accurate water quality forecasts at lower analytical costs is demonstrated by their R2 = 0.992 (RMSE = 1.693) for IWQI and 0.996 (RMSE = 0.822) for PS. This approach provides a cost-effective alternative to traditional methods by reducing the number of chemical parameters required for analysis. The results of this study offer significant insights for water resource management in arid and semi-arid regions, highlighting the potential of ML techniques in predicting irrigation water quality. The findings are valuable not only for Tunisia but also for similar regions worldwide, offering a tool for decision-makers to develop sustainable water management strategies and improve agricultural practices globally.