Detecting outliers in crowdsourced bathymetric data based on intelligently optimized 3D-DBSCAN algorithm

Abstract

To address the problem of low quality of the outlier detection results caused by the irregular spatial distribution of crowdsourced bathymetric data, an intelligently optimized 3D-DBSCAN method is hereby proposed for detecting outliers in crowdsourced bathymetric data. Firstly, the potential of the classic DBSCAN algorithm in clustering irregularly distributed data has been explored for three-dimensional space and upgraded to the 3D-DBSCAN algorithm. Meanwhile, three key parameters affecting the 3D-DBSCAN algorithm results have been determined, namely, the minimum number of objects in the neighborhood, the horizontal neighborhood radius and the vertical neighborhood radius. Then, the abilities of the K-nearest neighbor method and the genetic algorithm in setting the initial range of parameters and searching for optimal values have been comprehensively utilized. The optimal combination solution vector of the key parameters of 3D-DBSCAN under different distribution conditions has been adaptively acquired by defining and calculating the silhouette coefficient index. Finally, spatial clustering analysis on crowdsourced bathymetric data and intelligently detect outliers has been conducted by using the optimal combination solution vector as the input of the 3D-DBSCAN model parameters. The experimental results show that the proposed method can not only detect outliers in crowdsourced bathymetric data after adaptively optimizing the parameters, but also detect outliers by analyzing the distance relationship between points in the 3D space, thereby overcoming the limitations of traditional methods in detecting water depth anomalies at specific positions and irregularly distributed anomalies.