Importance of neighborhood parameters during clustering of bathymetric data using neural network

Abstract

The main component, which has a significant impact on safety of navigation, is the information about depth of a water area. The commonly used solution for depths measurement is usage the echosounders. One of the problems associated with bathymetric measurements is recording a large number of data. The fundamental objective of the author’s research is the implementation of a new reduction method for geodata to be used for the creation of bathymetric map. The main purpose of new reduction algorithm is that, the position of point and the depth value at this point will not be an interpolated value. In the article, author focused on importance of neighborhood parameters during clustering of bathymetric data using neural network (self-organizing map) – it is the first stage of the new method. During the use of Kohonen’s algorithm, the author focused on two parameters: topology and initial neighborhood size. During the test, several populations were created with number of clusters equal 25 for data collected from the area of 625 square meters (dataset contains of 28911 XYZ points). In the next step, statistics were calculated and results were presented in two forms: tabular form and as spatial visualization. The final step was their comprehensive analysis.