Suez Canal University

In the Wadi Umm Eish El-Zarqa area of Egypt, this study for the first time combines machine learning with EnMAP hyperspectral data, training Random Forest (RF) and Support Vector Machine with Radial Basis Function (SVM-RBF) models on pixel spectra from three known gold mining sites and expanding the training set with a 0.05 spectral probability tolerance, while using ALOS-PALSAR DEM to extract drainage networks linking upstream bedrock sources to downstream placers; ground-truth validation showed SVM-RBF had slightly higher overall accuracy (91.8% vs 89.

In the Wadi Umm Eish El-Zarqa area of Egypt, this study for the first time combines machine learning with EnMAP hyperspectral data, training Random Forest (RF) and Support Vector Machine with Radial Basis Function (SVM-RBF) models on pixel spectra from three known gold mining sites and expanding the training set with a 0.05 spectral probability tolerance, while using ALOS-PALSAR DEM to extract drainage networks linking upstream bedrock sources to downstream placers; ground-truth validation showed SVM-RBF had slightly higher overall accuracy (91.8% vs 89.