Methodology for Creating a Digital Bathymetric Model Using Neural Networks for Combined Hydroacoustic and Photogrammetric Data in Shallow Water Areas

Sensors (Basel). 2023 Dec 28;24(1):175. doi: 10.3390/s24010175.

Abstract

This study uses a neural network to propose a methodology for creating digital bathymetric models for shallow water areas that are partially covered by a mix of hydroacoustic and photogrammetric data. A key challenge of this approach is the preparation of the training dataset from such data. Focusing on cases in which the training dataset covers only part of the measured depths, the approach employs generalized linear regression for data optimization followed by multilayer perceptron neural networks for bathymetric model creation. The research assessed the impact of data reduction, outlier elimination, and regression surface-based filtering on neural network learning. The average values of the root mean square (RMS) error were successively obtained for the studied nearshore, middle, and deep water areas, which were 0.12 m, 0.03 m, and 0.06 m, respectively; moreover, the values of the mean absolute error (MAE) were 0.11 m, 0.02 m, and 0.04 m, respectively. Following detailed quantitative and qualitative error analyses, the results indicate variable accuracy across different study areas. Nonetheless, the methodology demonstrated effectiveness in depth calculations for water bodies, although it faces challenges with respect to accuracy, especially in preserving nearshore values in shallow areas.

Keywords: MLP neural network; UAV; USV; big data processing; data fusion; data reduction; digital bathymetric model; regression; shallow water area.

Grants and funding

This research outcome was financed by a subsidy from the Polish Ministry of Education and Science for statutory activities at the Maritime University of Szczecin (funding source numbers: s/1/s/KGiH/23, s/2/s/KGiH/23, 1/S/RD/WN/23, s/1/s/RB/23).