Proceedings Article | 27 March 2024
KEYWORDS: Education and training, Matrices, Roads, Random forests, Lawrencium, Statistical modeling, Engineering, Decision trees, Overfitting, Machine learning
In large scale landslide susceptibility assessment, it is necessary to reflect the differences of landslide formation background, conditions and evaluation indexes. However, the susceptibility assessment methods for small and medium scale area are not applicable. Sinan County has complex geological conditions, larger population and intense human engineering activity, in the event of a landslide, would be extremely costly. Thus, the site is a typical area for large scale and high precision susceptibility assessment. In order to explore locally adapted landslide susceptibility assessment method, 10 evaluation indicators, including altitude, slope, aspect, slope aspect, lithology, distance to fault, TWI, NDVI, distance to road and land types, are selected to evaluate landslide susceptibility, through Logistic Regression model (LR), Decision Tree model (DT) and Random Forest model (RF). Experimental results of model evaluation using receiver operating characteristics (ROC), area under the curve (AUC) and accuracy (ACC) showed that the RF (ROC=0.959, ACC=0.897) were more accurate than DT (ROC=0.925, ACC=0.884) and LR (ROC=0.786, ACC=0.703). High and very high landslide-prone areas are mainly concentrated in the steep slope of the valley bank, the geological environment is fragile, and the human engineering activities are strong. The results from this study demonstrates that the RF can identify landslides well in large-scale landslide susceptibility assessment, and provides scientific basis for disaster prevention and mitigation in large-scale landslide susceptibility assessment of mountain towns.
