Research on Non-Landslide Selection Method for Landslide Hazard Mapping

Most landslide prediction models need to select non-landslides. At present, non-landslides mainly use subjective inference or random selection method, which makes it easy to select non-landslides in high-risk areas. To solve this problem and improve the accuracy of landslide prediction, the method of selecting non-landslide by Information value (IV) is proposed in this study. Firstly, 230 historical landslides and 10 landslide conditioning factors are extracted and interpreted by using Remote Sensing (RS) image, Geographic Information System (GIS) and field survey. Secondly, random, buffer, river channel or slope, and IV methods are used to obtain non-landslides, and the obtained non-landslides are applied to the popular SVM model for landslide hazard mapping (LHM) in western area of Tumen City. The landslide hazard map based on the river channel or slope method is seriously inconsistent with the actual situation of study area, Therefore, the three methods of random, buffer, and IV are verified and compared by accuracy, receiver operating characteristic (ROC) curve and the area under curves (AUC). The results show that the landslide prediction accuracy of the three methods is more than 80%, and the prediction accuracy is high, but the IV is higher. In addition, IV can identify the very high hazard regions with smaller area. Therefore, it is more reasonable to use IV to select non-landslides, and IV method is more practical in landslide prevention and engineering construction. The research results may be useful to provide basic information of landslide hazard for decision makers and planners.

Landslide Hazard Mapping: GIS Based Susceptibility Assessment of Leoutikhola Watershed, Dhankuta, Nepal

This article examines the landslide hazard mapping in Leoutikhola watershed of Dhankuta district, eastern hills of Nepal. The hills are regarded as one of the most vulnerable areas in the world. The area is prone to different types of natural hazards such as landslide, debris flow, gully erosion, mass wasting, and flooding. Various studies pointed out biophysical and socioeconomic factors are more responsible to landslide hazards in the hill-slope environment. This paper argues that the rainfall intensity, unscientific methods of cultivation and lack of environment friendly developmental activities in steep slope are some of the major driving factors to landslide in the Leoutikhola watershed area. Leoutikhola watershed area has been suffering from different types of natural hazards such as landslide, debris flow, gully erosion, mass wasting and flooding. Field verification and satellite imageries are the major sources for the information to analyze. Due to its fragile and very weak geological condition, geomorphic hazard is considered more common than other types of hazards which lose massive lives and property in the study area. The field observation and local people perception indicate a large amount of budget required to mitigate natural hazards in this area especially for landslide. In this context, this paper attempts to portray the landslide hazard zonation and susceptibility mapping correlated with various factors. For this purpose, bivariate statistical index and Geographic Information System (GIS) and Remote Sensing (RS) tools are used to calculate weighted values. The overall parameters indicate that in the series (-4.2 to 4.58) of five different hazard zones like very low, low, moderate, high and very high. The paper concludes that hazard determination calculated values indicate to 4.2 to -2.09, -2.09 to -1.0, -1.0 to-0.05, -0.05 to 1.24 and 1.24 to 4.58 respectively in the range of quartile series.

Analysis of landslide hazard mapping of penang island malaysia using bivariate statistical methods

Landslide is one of the disasters which cause property damages, infrastructure destruction, injury and death. This paper presents the analysis of landslide hazard mapping of Penang Island Malaysia using bivariate statistical methods. Bivariate statistical methods are simple approach which are capable to produce good results in short computational time. In this study, three bivariate statistical methods, i.e. Frequency Ratio (FR), Information Value (IV) and Modified Information Value (MIV) are used to generate the landslide hazard maps of Penang Island. These bivariate statistical methods are computed using MATLAB tool. Landslide hazard map is categorized into 4 levels of hazard. The accuracy of each method and effectiveness in predicating landslides are validated and determined by using Receiver of Characteristics curve. The accuracies of FR, IV and MIV methods are 79.58%, 79.14% and 79.37% respectively.

Evidence Based Landslide Hazard Mapping in Purworejo using Information Value Model Approach

Purworejo District, which is located in Central Java, Indonesia, is prone to landslides. These are a natural hazard that often occur in mountainous areas, so landslide hazard analysis is needed to develop mitigation strategies. This paper elaborates on the use of an evidence-based statistical approach using the Information Value Model (IVM) to conduct landslide hazard mapping. The parameters of slope, aspect, elevation, rainfall, NDVI, distance from rivers, distance from the road network, and distance from faults were employed for the analysis, which was conducted based on a raster data environment, since the pixel is the most appropriate means to represent continuous data. Landslide evidence data were collected by combining secondary data and interpreting satellite imagery to identify old landslides. The IVM was successfully calculated by combining factors related to disposition to landslides and data on 19 landslide occurrences. The results helped produce a landslide susceptibility map for the northern and eastern parts of Purworejo District.