New paper Published by ReSEC Team members in Journal of Hydrology

Rahim Barzegar and Homa Kheyrollah published a paper titled “Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models”, which was recently published in the Journal of Hydrology.

The study is focused on developing accurate groundwater vulnerability maps is important for the sustainable management of groundwater resources using a combination of machine learning (ML) models, namely eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), and Random Forest (RF). Read more here