Seasonal Variation Influence to Water Image Properties to Retrieve Nearshore Bathymetry Based on Cloud Machine Learning Approach

This research aims to develop a computational framework for shallow water bathymetry reconstruction using machine learning-based Satellite-derived bathymetry (SDB) running on cloud computing. The RF and LR algorithms were tested for performance by considering the influence of seasonal variations. Both algorithms were trained using bathymetric data from hydrographic surveys, converted to the number of test and validation samples which determine the number independently. The accuracy test considering quantitative aspects through RMSE, MAE and R2, as well as qualitative aspects using cross-sectional transects of underwater topography and 1:1 plot. The complex bottom topography and supported by various benthic varieties causes differences in the water reflectance of in each season, it is necessary to analyze their influence on the machine learning algorithm in SDB. Overall, the best RMSE, MAE, and R2 were produced by the RF algorithm in transition season II with values of 0.34 m, 0.21 m, 0.944 respectively. For the LR algorithm, the best performance is shown in the east season with respective accuracies of 0.60 m, 0.46 m, 0.83. Through cross-sections of underwater topography, SDB algorithm can represent accurately in various geomorphological bottom variations, such as lagoons and reef flats. The LR algorithm is not yet able to optimally reconstruct shallow water bathymetry because outlier values in the accuracy test by 1:1 plot. In general, the RF and LR algorithms show high accuracy results at depths of up to 2 meters, and accuracy tends to decrease at depths > 3 meters. Through this study we found a relationship between the low reflectance of waters in the west season, which is correlated with the low performance of the SDB RF and LR algorithms. This study provides a cloud computing framework for the SDB reconstruction, efficiently in time and storage facilities without leaving any residue. The impressive archive facilities also enable multi-season analysis. © Geoinformatics International.