scholarly journals Landslide Susceptibility Mapping along Manipur-Assam NH-37

2021 ◽  
Vol 889 (1) ◽  
pp. 012002
Author(s):  
Sukhajit Khaidem ◽  
Kanwarpreet Singh

Abstract Landslides are a natural hazard in steep places that occur regularly and cause significant damage. To avoid and minimise hazards, comprehensive landslide remediation and control, landslide assessment, and hazard zonation are required. Various methods are established based on different assessment methodologies, which are essentially split into qualitative and quantitative approaches. GIS-based landslide susceptibility mapping was carried out along the National Highway 37, which connects Assam and Manipur and is a vital lifeline for the state, to identify and demarcate possible failure zones. A field visit was used to create a landslide inventory map along the road network. Google Earth and LANDSAT satellite imagery To perform landslide susceptibility zonation, thematic layers of several landslide causative elements were constructed in the study region. The study region has been divided into five endangered zones i.e. (“very low, low, moderate, high, and extremely high”). The landslide susceptibility zonation map was validated using the AUC and landslide density methods. The final map will be helpful to a variety of stakeholders, including town planners, engineers, geotechnical engineers, and geologists, for development and construction in the study region.

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Trung-Hieu Tran ◽  
Nguyen Duc Dam ◽  
Fazal E. Jalal ◽  
Nadhir Al-Ansari ◽  
Lanh Si Ho ◽  
...  

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.


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