Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio

2017 ◽  
Vol 10 (24) ◽  
Author(s):  
Kanwarpreet Singh ◽  
Virender Kumar
Keyword(s):  
Frequency Ratio ◽  
Himachal Pradesh ◽  
Landslide Hazard ◽  
Information Value ◽  
Hazard Mapping ◽  
National Highway ◽  
Landslide Hazard Mapping

Research on Non-Landslide Selection Method for Landslide Hazard Mapping

2021 ◽  
Author(s):  
Xia Li ◽  
Jiulong Cheng ◽  
Dehao Yu ◽  
Yangchun Han
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Selection Method ◽  
Landslide Hazard ◽  
River Channel ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Prediction ◽  
Conditioning Factors ◽  
Landslide Hazard Mapping

Abstract 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.

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

2019 ◽  
Vol 16 (2) ◽  
pp. 781
Author(s):  
Ilyas A Huqqani ◽  
Lea Tien Tay ◽  
Junita Mohamad Saleh
Keyword(s):  
Statistical Methods ◽  
Landslide Hazard ◽  
Simple Approach ◽  
Information Value ◽  
Computational Time ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Hazard Maps ◽  
Landslide Hazard Mapping ◽  
Landslide Hazard Map

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.

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

2019 ◽  
Vol 33 (1) ◽  
pp. 25-38 ◽  
Author(s):  
Sudaryatno Sudaryatno ◽  
Prima Widayani ◽  
Totok Wahyu Wibowo ◽  
Bagus Wiratmoko ◽  
Wahyu Nurbandi
Keyword(s):  
Landslide Hazard ◽  
Information Value ◽  
Mitigation Strategies ◽  
Slope Aspect ◽  
Hazard Mapping ◽  
Susceptibility Map ◽  
Evidence Based ◽  
Information Value Model ◽  
Landslide Hazard Mapping ◽  
Value Model

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.

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