ENHANCING LANDSLIDE RISK ASSESSMENT IN ALLEGHENY COUNTY, PENNSYLVANIA: A QUANTITATIVE SUSCEPTIBILITY ANALYSIS USING PROBABILITY DENSITY FUNCTION

In this study, we explore the susceptibility of landslides in Allegheny County, Pennsylvania, using an inventory of approximately 600 landslide occurrences compiled by the Pennsylvania Geological Survey. Our goal is to develop a predictive model to assess the likelihood of future landslides in the region. To achieve this, we compared the locations of landslide occurrences with various numeric data coverages of the study area to identify potential statistical correlations that might influence landslide susceptibility. We examined various raster datasets related to landforms, pedology, and geology, including slope, elevation, topographic position, slope aspect, vegetation canopy height, soil water storage, regolith thickness, proximity to mapped formation contacts, and fold axes.

Using a landslide correlation index, we quantified the relationship between the locations of landslides and randomly generated control datasets within the study area. By comparing these indices, we identified the most influential factors contributing to landslide risk. Unsurprisingly, we found that steep land surface slopes were the primary predictor of landslide occurrence. However, elevation, topographic position, vegetation, soil characteristics, and geologic structure also played significant roles in landslide development. Overburden thickness and hillslope direction had little effect on landslide occurrence.

By aggregating the indices for each coverage, we created a comprehensive landslide susceptibility map. This approach represents a crucial first step in developing a methodology to quantitatively assess landslide susceptibility. Our aim is to produce highly detailed landslide risk maps that can provide valuable insights to municipal planners and engineers for informed decision-making.

By employing probability density function analysis, our study bridges the gap between empirical landslide data and quantitative predictive models, enhancing our understanding of landslide susceptibility in Allegheny County, Pennsylvania. The outcomes of this research have the potential to significantly improve landslide risk assessment and management strategies in the region.