Integration of geographic features and bathymetric inversion in the Yangtze River's Nantong Channel using gradient boosting machine algorithm with ZY-1E satellite and multibeam data

Abstract

This study investigates the integration of geographic features from ZY-1E satellite data with advanced machine learning techniques to enhance water depth inversion in the Yangtze River's Nantong Channel. Utilizing the Gradient Boosting Machine (GBM) and its geospatially enhanced version, GBM-Lon./Lat., significant improvements in modeling precision were observed, as reflected by lower RMSE and higher R² values compared to traditional depth inversion methods. The research underscores the benefits of incorporating geospatial data, which allows for a more nuanced understanding of the hydrological dynamics and facilitates more accurate predictions in the turbid waters of the channel. Challenges such as atmospheric effects, water turbidity, and data acquisition issues under variable weather conditions were identified. The study proposes further optimization of these models to handle diverse environmental conditions and enhance the accuracy of bathymetric mapping. The integration of machine learning with remote sensing not only supports navigational safety and efficient waterway management but also contributes significantly to environmental monitoring and sustainable riverine infrastructure development.