Bimenyimana Theophile

Data Representation Shapes the Comparative Performance of XGBoost, Random Forest, and LSTM for Groundwater Head Prediction: A Case Study in Friuli Venezia Giulia, Italy

Reliable prediction of groundwater head is fundamental for sustainable aquifer management, yet the relative contributions of machine learning algorithms and input data representation remain poorly understood. This study systematically compares three widely used models, Extreme Gradient Boosting (XGBoost), Random Forest...