Enhanced soil salinity index prediction using hybrid stacking ensemble machine learning with explainable artificial intelligence (XAI) technique: a case study of the Nile Delta, Egypt

Satiprasad Sahoo, Chiranjit Singha, Ajit Govind. (25/11/2025). Enhanced soil salinity index prediction using hybrid stacking ensemble machine learning with explainable artificial intelligence (XAI) technique: a case study of the Nile Delta, Egypt. Discover Applied Sciences, 8.

Abstract

Soil salinity represents the leading form of land degradation in arid and semi-arid regions. This study employed five hybrid stacking ensemble (SE) machine learning models (SE-GBM, SE-RF, SE-SVM, SE-XGB, and SE-MARS) to map salinity distribution across Egypt’s Nile Delta for 2023 and projected conditions for 2030, using EC-Earth3 and MIROC6 CMIP6 climate scenarios under SSP2-4.5 and SSP5-8.5. Results reveal substantial differences between scenarios, with SSP5-8.5 indicating up to a 15% higher salinity increase in the eastern Delta compared to SSP2-4.5. This highlights its reliability for assessing future salinity dynamics across the Nile Delta. Model validation confirmed that the SE-GBM model achieved the highest accuracy in predicting soil salinity, with an R² of 0.396 and RMSE of 0.061. Except for MARS model due to their low accuracy, all models indicated that the north-eastern, eastern, and south-eastern Nile Delta had the highest soil salinity in 2023. Salinization in these zones is driven by climate change, seawater intrusion, poor irrigation, and human pressures. Boruta analysis highlighted pH as the most influential predictor, while bulk density was least significant. SHAP (SHapley Additive exPlanations) results further showed precipitation and clay content as key drivers of salinity variability. These findings underline the robustness of Machine learning models in capturing complex soil–climate interactions. Future work should expand applications in globally, especially in resource-constrained regions.