Estimation of spatial soil depth and its application for shallow landslides and debris flow assessment: case study at Mt. Jiri, S. Korea

Soil depth plays critical role in prediction studies reflecting hydrologic mechanism such as shallow landslide and debris flow although there are many parameters. Thus, many researchers are studying the estimation of soil depth distribution using various methods such as a kriging and artificial neural networks (ANNs) since it is not easy to get a detailed soil depth distribution in field. The aims of this study are 1) to estimate detailed spatial distribution of soil depth (various methods such as ANNs, Kriging, s- and z-model, and c-model) and, 2) to apply them for assessment of shallow landslide instability and debris flow. To do this, soil depth of 760 points using knocking pole test method and elevation datasets using GPS-RTK were collected at Mt Jiri, South Korea. To analysis the accuracy of each estimated soil depth distribution, the lowest root mean square error (RMSE), mean absolute error (MAE) and the highest values of the coefficient of determination (R²) were applied and, ANNs method showed reasonable result better than did others. In the effect of shallow landslide instability and debris flow assessment with the each soil depth distribution results, soil depth distribution using an ANNs method also showed high simulated model performance by modified success ratio (MSR). These results indicated that ANNs can be one of the methods to estimate the soil depth distribution for improvement of accuracy of shallow landslide instability mapping and debris flow assessment.