| Abstrak/Abstract |
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. |
