Application of artificial intelligence methods during the processing of spatial data from the hydrographic systems for coastal zone

Abstract. Effective processing of spatial data in coastal zones requires the integration of measurements from various sensors to achieve a more comprehensive picture of dynamic environmental changes. This study proposes a new approach to spatial data analysis, combining information from the LiDAR system and multi-beam echo sounder (MBES). This combination of both sources allowed for a more accurate estimation of the topography and bathymetry of the coastal zone. A key element of the study was developing an original data reduction method based on Self-Organizing Maps (SOM) neural networks. Initially used for analysing bathymetric data, this method has been optimised for aquatic data, enabling effective processing of both heights from LiDAR and depths from MBES. Data reduction significantly shortened computation time – interpolation using the Empirical Bayesian Kriging (EBK) method for raw data took over 9 hours, whereas, for the reduced data (those with the highest density), it took just 4 minutes and 51 seconds while maintaining the comparable quality of results. The study confirmed that the reduced data meets the requirements of the International Hydrographic Organization (IHO) for shallow water bodies, which indicates the high accuracy of the method employed. The results suggest that data reduction based on artificial intelligence allows for the efficient management of big spatial data, and its integration with classical GIS interpolation methods can find broad applications in hydrography, environmental monitoring, and coastal zone management.