Landslide hazard mapping in Nepal: case studies from Lothar Khola (central Nepal) and Syangja district (western Nepal)

The present paper attempts to evaluate the present status of hazard mapping in Nepal and describes the case studies of landslide hazard mapping of the Lothar Khola (central Nepal) and Syangja district (western Nepal) by two different methods: 1. The rating method proposed in the Mountain Risk Engineering (Deoja et al. 1991), and 2. Bivariate Statistical method developed by the Institute of Aerospace Survey and Earth Sciences (ITC). The Netherlands (Van Westen 1997). The first method is a manual one and used to make hazard map of the Lothar Khola watershed while the second one is GIS based and was utilized to produce hazard map of the Syangja district. Potentially unstable slopes were mainly found on the slopes ranging from 26-40°, residual soil cover, and in areas underlain by the slate and phyllite. Interestingly the slope movement is high in the areas covered by forest in comparison to the cultivated slopes.

Download Full-text

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i2.pp781-786 ◽

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.

Download Full-text

Landslide Hazard Mapping in Panchase Mountain of Central Nepal

Environment and Natural Resources Journal ◽

10.32526/ennrj.18.4.2020.37 ◽

2020 ◽

Vol 18 (4) ◽

pp. 387-399

Author(s):

Padam Bahadur Budha ◽

◽

Pawan Rai ◽

Prem Katel ◽

Anu Khadka ◽

...

Keyword(s):

Landslide Hazard ◽

Hazard Mapping ◽

Central Nepal ◽

Landslide Hazard Mapping

Download Full-text

Landslide hazard mapping in the Nallu Khola watershed, central Nepal

Journal of Nepal Geological Society ◽

10.3126/jngs.v21i0.32145 ◽

2000 ◽

Vol 21 ◽

Author(s):

Jagannath Joshi ◽

Stefan Majtan ◽

Koichi Morita ◽

Hiroshi Omura

Keyword(s):

Drainage Density ◽

Landslide Hazard ◽

Field Conditions ◽

Hazard Mapping ◽

Slope Gradient ◽

Central Nepal ◽

High Hazard ◽

Landslide Hazard Mapping ◽

Landslide Hazard Map ◽

Slope Class

This paper deals with landslide hazard mapping in the Nallu Khola watershed of Central Nepal. The study reveals that slope class 30o-40o is highly susceptible to sliding. The highest landslide density is seen on 35° slope with drainage frequency of 40-50 no./km2 Similarly, the lowest landslide density is found associated with the lowest average slope gradient and lowest drainage density. Landslide hazard map shows that the high, medium, and low hazard areas occupy respectively 20%, 45%, and 35% of the watershed. Similarly, the landslide density is the highest in the cells that are categorised as high hazard. The highest number of landslide containing cells in high hazard rank suggests that the forecasted hazard ranks nearly match with the present field conditions. But there are some areas, where forecasted hazard ranks do not match with the present field conditions.

Download Full-text

Research on Non-Landslide Selection Method for Landslide Hazard Mapping

10.21203/rs.3.rs-270737/v1 ◽

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.

Download Full-text