Groundwater Salinity Prediction in the African Continent Using Machine Learning Techniques and Methods

This chapter details a study using Machine Learning (ML) to predict rising groundwater salinity across Africa, a critical threat to water security. Traditional models face challenges with data scarcity and computational demands. By analyzing continent-wide data on climate, soil, and hydrogeology, the research tested multiple ML algorithms. Ensemble methods, particularly Random Forest and XGBoost, proved most accurate in predicting key salinity indicators (EC and TDS), significantly outperforming traditional linear models. Analysis identified nitrogen content (often from agriculture), sand fraction, and aridity as primary drivers of salinity, highlighting a mix of human and natural causes. The study establishes a robust ML framework for salinity prediction in data-scarce regions and bridges the gap between advanced modeling and practical water management policy, offering a vital tool for informed decision-making.