Machine Learning-Based Assessment of Watershed Morphometry in Makran

This study proposes an artificial intelligence approach to assess watershed morphometry in the Makran subduction zones of South Iran and Pakistan. The integrates machine learning algorithms, including neural networks (ANN), support vector regression (SVR), multivariate linear (MLR), on a single platform. area was analyzed by extracting watersheds from Digital Elevation Model (DEM) calculating eight morphometric indices. parameters were normalized using fuzzy membership functions improve accuracy. performance algorithms is evaluated mean squared error (MSE), absolute (MAE), correlation coefficient (R2) between output method actual dataset. ANN model demonstrated high accuracy with R2 value 0.974, MSE 4.14 × 10?6, MAE 0.0015. results compared tectonic characteristics area, indicating potential for utilizing algorithm similar investigations. offers novel way ML techniques, which may have advantages over other approaches.