GIS-Based Landslide Susceptibility Modelling in Urbanized Areas: A Case Study of the Tri-City Area of Poland

This paper presents the results of landslide prediction modelling for young glacial areas performed using statistical methods. The area in question is urbanized and therefore mass wasting activity is a matter of interest to both the local community and the authorities. The analysis was based on the 2011 ‘Register of landslides and areas prone to mass movements with a scale of 1:10,000 for the city of Gdansk’ and the 2012 incomplete ‘Register of landslides and areas prone to mass movements with a scale of 1:10,000 for the city of Gdynia’. The research took into account geological, geomorphological, hydrological, hydrogeological, and anthropogenic conditions. The landslide susceptibility map was created using the statistical landslide index. The calculated indices were used to create a map of Gdansk’s landslide susceptibility. In Gdansk, 84.50% of the total diagnosed landslide area belongs to the high susceptibility class, 14.25% to the moderate susceptibility class, and only 1.25% to the low or very low susceptibility class. After extrapolation, the data was also used to create a susceptibility map for the remaining parts of the Tri-City area, Sopot and Gdynia. The difficulty of extrapolating landslide data for neighboring urban areas was indicated. In Gdansk, which had been covered by geological mapping, the best modelling results were obtained with a large number of causal factors. In Gdynia and Sopot, for which the statistical landslide index value was extrapolated from Gdansk, the best results were obtained when selected causal factors were considered. In Sopot and Gdynia, 81.6% of the landslide area belongs to the high susceptibility class, 15.1% to the moderate class, and 3.3% to the low susceptibility class. These results emphasize a different role of some causal factor classes in the occurrence of landslides in neighboring urban areas. The resultant maps show the areas in which mass wasting is the most probable in the future.