Landslide hazard assessment in Tinh Tuc town, Cao Bang province, Vietnam using Frequency ratio method and the combined Fractal-frequency ratio method - Binh Van Duong; Igor K. Fomenko; Kien Trung Nguyen; Oleg Vladimirovich Zerkal; Olga N. Sirotkina; Dang Hong Vu (Записки Горного института, 2024)
Landslides are one of the most frequent natural disasters that cause significant damage to property in Vietnam, which is characterized by mountainous terrain covering three-quarters of the territory. In 17 northern mountainous provinces of the country, over 500 communes are at a high to very high landslide hazard. The main goal of this study was to establish landslide hazard maps and conduct a comparative evaluation of the efficiency of the methods employed in Tinh Tuc town, Cao Bang province. The landslide hazard assessment was carried out in this study using the combined Fractal-frequency ratio (FFR) and the Frequency ratio (FR) methods. The FR method is based on the actualist principle, which assumes that future landslides may be caused by the same factors that contributed to slope failure in the past and present. The FFR method is based on the determination of the fractal dimension, which serves as a measure of the landslide filling density in the study area. Eight landslide-related factors were considered and presented in cartographic format: elevation, distance to roads, slope, geology, distance to faults, land use, slope aspect, and distance to drainage. Determining the area under the receiver operating characteristic curve (ROC-AUC) and verification index (LRclass) was performed to assess the performance of prediction models and the accuracy of the obtained maps. As a result, five zones were identified for the study area, characterized by very low, low, moderate, high, and very high landslide hazards. The analysis of the reliability of the obtained landslide hazard maps using the AUC and LRclass indices revealed that the FFR model has a higher degree of reliability (AUC = 86 %, LRclass = 86 %) compared to the FR model (AUC = 72 %, LRclass = 73 %); therefore, its use is more effective.