Durmaz, Muhammet
Physically-based models are reliable tools for landslide susceptibility assessment. However, they are, based on various simplifications, affected by different factors, and applicable for particular landslides. Therefore, landslide inventories are recommended to be subdivided into subsets reflecting different controlling factors and failure mechanisms, and the analysis should reflect these differences. Many physically-based models are based on infinite slope assumptions. The applicability of this assumption is associated with having large and long dimensions compared to their depth. This study investigates morphometric features of more than 1000 individual landslides triggered by a rainfall event on Sept 27- Oct 4 in 2017 in Kaptanpaşa, Rize (Northern Turkey). The failures were classified according to their morphology, slope types, and whether the infinite slope assumption is valid or not. 45% of failures on hillslopes and 24.1% of failures in other slopes confirm the infinite slope assumption. Depth of landslide can be represented by Weibull distribution with a mean depth of 1.05 m. Two different hydrological (FSLAM, TRIGRS) and stability (infinite-slope, 3D slope stability via SCOOPS3D) methods are tested for physically-based modeling of these failures. Stability parameters (ø,c,z) and antecedent conditions were calibrated semi-automatically via a MATLAB code. Then hydraulic parameters were calibrated iteratively. In the calibration, only failures confirming the models' assumptions were employed. The performance of FSLAM was evaluated as highest in terms of area under curve (AUC), indicating that the final pore pressure condition is close to saturation. SCOOPS3D estimated unstable areas less than infinite slope solution with higher ratio of True Positive Rate (TPR) / False Positive Rate (FPR) , confirming the other studies in the literature. Moreover, the performance of the stability assumption is shown to be dependent on the morphology of failures.


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Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR) ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment. However, to assess the probability of landslide within a specified period of time and within a given area, it is important to understand th...
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Citation Formats
M. Durmaz, “PHYSICALLY-BASED MODELING OF RAINFALL-TRIGGERED LANDSLIDES IN KAPTANPASA, RIZE,” M.S. - Master of Science, Middle East Technical University, 2022.