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METHODS article

Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1471695

Enhancing Water Depth Inversion Accuracy in Turbid Coastal Environments Using Random Forest and Coordinate Attention Mechanisms

Provisionally accepted
Siwen Fang Siwen Fang 1*Zhongqiang Wu Zhongqiang Wu 1*Shulei Wu Shulei Wu 1*Zhixing Chen Zhixing Chen 2wei shen wei shen 3*zhihua mao zhihua mao 4*
  • 1 Hainan Normal University, Haikou, China
  • 2 South-Central Minzu University, wuhan, China
  • 3 Shanghai Ocean University, Shanghai, Shanghai Municipality, China
  • 4 Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    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

    Keywords: Water depth inversion, random forest, Coordinate attention, Bathymetric mapping, Turbid waters

    Received: 28 Jul 2024; Accepted: 09 Oct 2024.

    Copyright: © 2024 Fang, Wu, Wu, Chen, shen and mao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Siwen Fang, Hainan Normal University, Haikou, China
    Zhongqiang Wu, Hainan Normal University, Haikou, China
    Shulei Wu, Hainan Normal University, Haikou, China
    wei shen, Shanghai Ocean University, Shanghai, 130012, Shanghai Municipality, China
    zhihua mao, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, 310012, Zhejiang Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.