Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method

Abstract

Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.

The Use of Colorimeters to Support Remote Sensing Techniques on Asphalt Pavements

“…The visualization of the reflectance and the normalization equation/filter values was achieved using interpolation. The ArcGIS software was used for the application of data using a kriging interpolation tool, which works through the correlation between the surrounding values' distance and the values located around a point [46,47]. It should be noted that this can be considered a starting point of the described methodology since it took place in a small region without any satellite image.…”

Improving slope stability estimates by incorporating geophysical and remote sensing monitoring data into hydro-geomechanical modeling

“…Numerous researches showed that root cohesion can be drastically reduced following fire lading to slopes more prone to failures (Gehring et al, 2019;Jackson and Roering, 2009;Lanini et al, 2009) The spatial and temporal uncertainty of these input parameters is still one of the major challenges in predicting landslides (Anagnostopoulos et al, 2015;Sidle and Ochiai, 2006;van Westen et al, 2006). To consider this uncertainty, a probabilistic approach can be used (Hammond et al, 1992;Lee et al, 2020;Nilsen, 2000;Strauch et al, 2018). Strauch et al ( 2018) developed a regional model of probabilistic slope failures and applied it to the North Cascades National Park Complex in the state of Washington, USA.…”

Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping

“…Each study also used different input variables with consideration of classifying the categories of geographical conditions and the obtained values were used for the purpose. In addition, Levenberg–Marquardt and Bayesian regularization algorithms were used as a back propagation method, which has a crucial influence on the outcome of ML 2 , 3 , 19 in finding an optimal weight and bias for prediction 20 . Each method is developed to minimize errors when finding the inflection point through a probability technique.…”