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essential for various applications in port management, navigation safety, marine engineering, and environmental monitoring. Satellite remote sensing data can rapidly acquire the bathymetry of the target shallow waters, and researchers have developed various models to invert the water depth from the satellite data. Geographically weighted regression (GWR) is a common method for satellite-based bathymetry estimation. However, in sediment-laden water environments, especially ports, the suspended materials significantly affect the performance of GWR for depth inversion. This study proposes a novel approach that integrates GWR with Random Forest (RF) techniques, using longitude, latitude, and multispectral remote sensing reflectance as input variables. This approach effectively addresses the challenge of estimating bathymetry in turbid waters by considering the strong correlation between water depth and geographical location. The proposed method not only overcomes the limitations of turbid waters but also improves the accuracy of depth inversion results in such complex aquatic settings. This breakthrough in modeling has significant implications for turbid waters, enhancing port management, navigational safety, and environmental monitoring in sediment-laden maritime zones.<\/jats:p>","DOI":"10.3390\/s24123802","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T08:45:50Z","timestamp":1718181950000},"page":"3802","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Addressing Challenges in Port Depth Analysis: Integrating Machine Learning and Spatial Information for Accurate Remote Sensing of Turbid Waters"],"prefix":"10.3390","volume":"24","author":[{"given":"Xin","family":"Li","sequence":"first","affiliation":[{"name":"School of Marine Science, Shanghai Ocean University, Shanghai 201306, China"},{"name":"Marine Surveying and Mapping Engineering and Technology Research Center, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6720-1104","authenticated-orcid":false,"given":"Zhongqiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Hainan Normal University, Haikou 571158, China"},{"name":"States Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China"}]},{"given":"Wei","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Marine Science, Shanghai Ocean University, Shanghai 201306, China"},{"name":"Marine Surveying and Mapping Engineering and Technology Research Center, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1109\/TGRS.2012.2218818","article-title":"Combined Effect of Reduced Band Number and Increased Bandwidth on Shallow Water Remote Sensing: The Case of WorldView 2","volume":"51","author":"Lee","year":"2013","journal-title":"IEEE Trans. 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