Infinite slope stability model and steady-state hydrology-based shallow landslide susceptibility evaluations: The Guneysu catchment area (Rize, Turkey)

The main purpose of this study is to investigate shallow landslide susceptibility by considering the infinite slope stability model and steady-state hydrological conditions. The prediction performance of the Stability Index Mapping (SINMAP) carried out by considering the mechanical and hydrological parameters and soil thickness values for different residual soils derived from different geological formations was evaluated. For this purpose, comprehensive geotechnical site investigations were conducted in the Guneysu catchment area located at the east of Rize in the Eastern Black Sea region of Turkey, where shallow landslides are frequently observed within the residual levels developed as a result of decomposition of magmatic rocks. The research was performed in four stages: (i) The general characteristics of the region were examined; (ii) detailed investigations were carried out for the preparation of shallow landslide inventory of the Guneysu catchment area; (iii) to reveal the mechanical and hydrological characteristics of the residual soils, disturb and undisturbed samplings were carried out, and geophysical investigations were performed; (iv) as the last stage, shallow landslide susceptibility was assessed by implementing the SINMAP mathematical model. As a result, the average accuracy value of the models produced to predict shallow landslide initiation in the region was obtained to be 96.7%. Considering the statistics achieved in this research, it is realized that the differentiation of soil material increases the model prediction capacity. Additionally, pre-evaluations of the mechanical and hydrological characteristics of the residual soils also increase the estimation performance.

Automated summary

This study investigates shallow landslide susceptibility using the SINMAP model in the Guneysu catchment area, with an average accuracy of 96.7%. The differentiation of soil materials and pre-evaluations of mechanical and hydrological characteristics improve the prediction performance.