AUTHOR=Fang Siwen , Wu Zhongqiang , Wu Shulei , Chen Zhixing , Shen Wei , Mao Zhihua TITLE=Enhancing Water depth inversion accuracy in turbid coastal environments using random forest and coordinate attention mechanisms JOURNAL=Frontiers in Marine Science VOLUME=11 YEAR=2024 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1471695 DOI=10.3389/fmars.2024.1471695 ISSN=2296-7745 ABSTRACT=

This study introduces an innovative water depth estimation method for complex coastal environments, focusing on Yantian Port. By combining Random Forest algorithms with a Coordinate Attention mechanism, we address limitations of traditional bathymetric techniques in turbid waters. Our approach incorporates geographical coordinates, enhancing spatial accuracy and predictive capabilities of conventional models. The Random Forest Lon./Lat. model demonstrated exceptional performance, particularly in shallow water depth estimation, achieving superior accuracy metrics among all evaluated models. It boasted the lowest Root Mean Square Error (RMSE) and highest coefficient of determination (R²), outperforming standard techniques like Stumpf and Log-Linear approaches. These findings highlight the potential of advanced machine learning in revolutionizing bathymetric mapping for intricate coastal zones, opening new possibilities for port management, coastal engineering, and environmental monitoring of coastal ecosystems. We recommend extending this research to diverse coastal regions to validate its broader applicability. Additionally, exploring the integration of additional geospatial features could further refine the model’s accuracy and computational efficiency. This study marks a significant advancement in bathymetric technology, offering improved solutions for accurate water depth estimation in challenging aquatic environments. As we continue to push boundaries in this field, the potential for enhanced coastal management and environmental stewardship grows, paving the way for more sustainable and informed decision-making in coastal zones worldwide.