hydropython/FFused-SH-ML

This repository evaluates Gradient Boosting, Random Forest, XGBoost, and Multilayer Perceptron using three input scenarios at three meteorological stations in Ethiopia's Awash basin. Outstanding results are achieved across all stations with IS1 and IS2 inputs, highlighting model robustness. # Fusing-Remotely-sensed-Sunshine-Hour-and-temperature-features-with-Machine-learning-Algorithms- This repository evaluates Gradient Boosting, Random Forest, XGBoost, and Multilayer Perceptron using three input scenarios at three meteorological stations in Ethiopia's Awash basin. Outstanding results are achieved across all stations with IS1 and IS2 inputs, highlighting model robustness.