Methods of Cluster Analysis for Detection of Uniform Displacement Zones of Landslides and Anti-Landslide Structures


cluster analysis
clustering method
similarity measure
horizontal displacement

How to Cite

Andrii, K., & Roman, S. (2023). Methods of Cluster Analysis for Detection of Uniform Displacement Zones of Landslides and Anti-Landslide Structures. Journal of Basic & Applied Sciences, 19, 151–162.


The primary goal of landslide monitoring is the development and implementation of appropriate prediction models. Such models will allow forecasting of the anticipated landslide movements and failures. The deployment of these models is only possible by the results of geospatial monitoring. However, the measured displacements of the monitoring targets mostly have different values that may deviate a couple of times for different parts of the observed landslide. Therefore, the correct prediction model can be developed for the points with similar displacements, or in other words, for the points with the same displacement velocities. The grouping of points with similar values is known as clustering or zoning task. Having the groups of similar displacements, it is possible to work out the proper prediction model for each group of displacements and detect the probable blunders in the measurements. The paper outlines the results of geospatial monitoring for landslide and anti-landslide structures carried out for small-scale landslide and a system of retaining walls in Kyiv, Ukraine. The efficiency of cluster analysis for uniform displacement zone identification has been studied by the results of geospatial monitoring. The basic principles and ideas of cluster analysis and clustering methods have been given. The different clustering methods have been examined. Each clustering method's efficiency has been estimated by distance determination methods and similarity measures. The quantitative analysis of the considered clustering methods was checked by evaluation analysis. The most reliable results in a line of the study have demonstrated centroid clustering and furthest neighbor clustering. The determined similarity measures for those two methods were almost the same.


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