An improved method for water depth mapping in turbid waters based on a machine learning model

Abstract

Water depth data products are fundamental for the study of near-shore marine science and coastal engineering; therefore, it is essential to identify the optimal technique for coastal water depth mapping. There are reliable mapping techniques for clear open waters. However, near-shore waters is typically turbid, which decreases the accuracy of previous methods for clear waters. To address these challenges, in this study, a machine learning-based topographic inversion method for turbid waters was developed. The log band ratio algorithm presented in previous studies was modified initially to enhance its application in turbid waters. Then, we established a model called AdaBoost-GBDT for bathymetric inversion in turbid waters using the Gradient Boosting Decision Tree (GBDT) algorithm fused with the Adaptive Boosting (AdaBoost) algorithm. Comparison of the results revealed that the model is highly reliable in all water depth intervals. To evaluate the quality of our model derived product in coastal research, a comparison of the data quality among four water depth datasets was conducted in this study. The comparison demonstrated that the inversion results of AdaBoost-GBDT have the best data coverage and accuracy. Furthermore, when applied to the Finite Volume Community Ocean Model (FVCOM), the AdaBoost-GBDT bathymetric product had the best performance. This study provides a new method for the bathymetric inversion of turbid waters, which can contribute to coastal management and engineering.