Soil erosion susceptibility assessment in the Bistrita River basin (Romania) using machine learning algorithms and GIS.

ABSTRACT: This study evaluates the relationship between soil erosion and 12 environmental and anthropogenic variables to identify erosion-susceptible areas in the Bistrita River basin (Romania), using machine learning algorithms (MLA). Three supervised classification algorithms Support Vector Machine (SVM), K-Nearest Neighbors (K-NN) and Random Forests (RF) were trained using 4761 sets of values, 1191 values to validate the models, and 1488 to test the models and to ensure that they could be applied in practice. The performance of each model was evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). Using natural breaks, the results were spatially represented using 5 erosion susceptibility classes (very high, high, moderate,

SUBMITTER: Muratoreanu G 

PROVIDER: S-EPMC12728231 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature