Performance of two neural network models in bathymetry

Neural networks are widely used as predictors in several fields of applications, such as prediction of shallow water depth. The purpose of this study is to investigate the performance of two artificial neural networks models as potential methods in bathymetry. A comparison approach is used to evaluate network models, the regression tree and an inversion model. The high-resolution IKONOS and moderate-resolution Landsat satellite images serve as the case studies, and results based on the root mean square errors and coefficient of determination (R2) show that artificial neural networks outperform the inversion model and the regression tree.