Hybrid Artificial Neural Networks for Modeling Shallow-Water Bathymetry via Satellite Imagery

Accurate bathymetric mapping for shallow-water areas is essential for coastal and maritime engineering applications. However, traditional multibeam or light detection and ranging (LiDAR) survey techniques used to produce high-quality bathymetric maps are expensive. Satellite-derived bathymetry provides a fast and inexpensive method for the large-scale mapping of shallow-water areas and can overcome the complexities of traditional bathymetric mapping methods in these areas. Traditionally, linear regression models, most commonly the Stumpf model, are used for satellite-based bathymetric modeling. However, nonlinear artificial neural network (ANN) models have been recently developed and implemented for satellite-based bathymetric modeling and are under significant investigation to develop the most accurate and optimal model. This article proposes two new hybrid ANN-based models for bathymetric modeling and investigates their performance using satellite imagery data and ``truth'' depth data for a coastal shallow-water study area. Two-hybrid ANN algorithms are developed, namely, particle swarm optimization (PSO)-ANN and optimally pruned extreme learning machine (OPELM), and their results are compared with the traditional Stumpf method and current state-of-the-art ANN model. The study area dataset comprises the ``truth'' depth data from a nautical chart of the Alqumriyah Island study area in Saudi Arabia and the corresponding spectral reflection values of green, blue, and near-infrared bands from the free-of-charge Level-1C product of Sentinel-2A images used to train and validate the two newly developed models and the traditional models. The results show that the developed OPELM method can accurately derive the bathymetry and is superior to the developed PSO-ANN model, the current state-of-the-art ANN model, and the traditional Stumpf model by 12.10%, 18.76%, and 32.46%, respectively. The OPELM model can also be used for bathymetric modeling of shallow-water areas with depths up to 30 m with a high level of accuracy compared with the current state-of-the-art ANN and traditional methods. The significant contribution of this research is that it is the first investigation of the artificial intelligence-based hybrid OPELM method for accurate bathymetric modeling and will certainly encourage further investigations of hybrid models. Moreover, this research explores whether these developed hybrid models can meet the International Hydrographic Organization standards for hydrographic survey applications.