Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

Abstract

The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 9 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.

Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

“…All of the above factors and landslides were then entered into the GIS medium using ArcGIS 10.1 software. The process of converting these continuous variables into categorical classes were conducted using expert opinions [39] and Jenks [40] natural breaks to define class intervals. In GIS, point data can be described as a singular (X,Y) coordinate, which does not reflect landslide affected areas, as this type of feature is usually used when the areal extent of a small landslide cannot be drawn due to the scale of the map [36].…”

“…All of the above factors and landslides were then entered into the GIS medium using ArcGIS 10.1 software. The process of converting these continuous variables into categorical classes were conducted using expert opinions [39] and Jenks [40] natural breaks to define class intervals. Lithology is considered as one of the most important factor because it influences the geomechanical characteristics of terrain (e.g., static and dynamic friction, restitution characteristics and fragmentation ratio), therefore controlling the types and mechanism of rockfalls [41].…”

Forecast Model of Urban Stagnant Water Based on Logistic Regression

“…Since the OR has some good properties in measuring the association [9], it can be used to describe the effect of the independent variables on the event probability in the logistic regression model, Therefore, it is often used to interpret the regression coefficients of the logistic regression model [10].…”

Landslide Susceptibility Mapping in Vertical Distribution Law of Precipitation Area: Case of the Xulong Hydropower Station Reservoir, Southwestern China

“…Some researchers have recently applied different classification methods to LSM prediction. Many statistical methods exist for LSM, such as the logistic regression (LR) method [13][14][15][16][17], statistical index (SI) [18,19], discriminant analysis (DA) [20] and bivariate statistical analysis (BSA) [21]. However, the statistical approaches have very strict mathematical reasoning, and their application must meet strict demands.…”