scholarly journals Landslide Hazard Assessment Using Probabilistic and Statistical Approaches: A Case Study of Chamba Region, Himachal Pradesh, India

2020 ◽  
Author(s):  
Desh Deepak Pandey ◽  
Rajeswar Singh Banshtu ◽  
Kanwarpreet Singh ◽  
Laxmi Devi Versain
Keyword(s):  
Analytical Hierarchy Process ◽  
Landslide Hazard ◽  
Information Value ◽  
Process Models ◽  
Mitigation Measures ◽  
Slope Aspect ◽  
Hazard Zonation ◽  
Hazard Maps ◽  
Zonation Maps ◽  
Hierarchy Process

Abstract Landslides have adversely affected the southern region of Chamba district during past three decades. To minimize the damage to ecology and environment due to such natural calamities, landslide hazard zonation and mitigation measures are essential component to stabilize the natural slopes and other physiographic features. In order to remodeling lopsidedness in study area analytical hierarchy process and information value methods with applications of remote sensing and geographic information system (GIS) are utilized to delineate the most recumbent landslide hazard zones. Eleven-factor maps like slope gradient, slope aspect, relative relief, land use/ cover etc., were delineated using different sets of data like satellite images and field investigations etc. Depending upon the severity, landslide hazard maps (LHZ) were further divided based upon information value method and analytical hierarchy process models respectively, into five different categories very low (1.2% and 2.95%), low (5.31% and 4.27%), moderate (24.40% and 20.03%), high (29.26% and 31.03%), and very high (40.30% and 44.2%). These hazard maps obtained through both information value and analytical hierarchy process (AHP) were compared for accuracy using success rate curve (SRC) method. Accuracy of the hazard zonation maps was found to be 78.62% for AHP and 85.17% for Inf. Value models.

scholarly journals LANDSLIDE HAZARD ZONATION MAPPING OF CHAMOLI LANDSLIDES IN REMOTE SENSING AND GIS ENVIRONMENT

2018 ◽  
Vol XLII-5 ◽  
pp. 475-480
Author(s):  
M. K. Tripathi ◽  
H. Govil ◽  
P. K. Champati ray ◽  
I. C. Das
Keyword(s):  
Remote Sensing ◽  
Remote Sensing And Gis ◽  
Landslide Hazard ◽  
Information Value ◽  
Slope Aspect ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Thematic Layers ◽  
Zonation Map ◽  
Gis Environment

<p><strong>Abstract.</strong> Landslides are very common problem in hilly terrain. Chamoli region of Himalaya is highest sensitive zone of the landslide hazards. The purpose of Chamoli landslide study, to observe the important terrain factors and parameters responsible for landslide initiation. Lithological, geomorphological, slope, aspect, landslide, drainage density and lineament density map generated in remote sensing and GIS environment. Data information of related geological terrain obtain through topographic maps, remote sensing images, field visits and geological maps. Geodatabases of all thematic layers prepared through digitization of topographic map and satellite imageries (LISS-III, LISS-IV &amp;amp; ASTER DEM). Integrated all thematic layers applying information value method under GIS environment to map the zonation of landslide hazard zonation map validation and verification completed by field visit. The landslide hazard zonation map classified in four classes very high, high, medium and low.</p>

Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia

2021 ◽  
Vol 13 (1) ◽  
pp. 1668-1688
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamrat Mekuria ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Analytical Hierarchy Process ◽  
Flood Hazard ◽  
Google Earth ◽  
Information Value ◽  
Lake Tana ◽  
Ahp Model ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Hierarchy Process

Abstract The flood is one of the frequently occurring natural hazards within the sub-basin of Lake Tana. The flood hazard within the sub-basin of Lake Tana causes damage to cropland, properties, and a fatality every season. Therefore, flood susceptibility modeling in this area is significant for hazard reduction and management purposes. Thus, the analytical hierarchy process (AHP), bivariate (information value [IV] and frequency ratio [FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field survey, historical document, and Google Earth Imagery, 1,404-flood locations were determined, classified into 70% training datasets and 30% testing flood datasets using a subset within the geographic information system (GIS) environment. The statistical relationship between the probability of flood occurrence and 11 flood-driving factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by summing all weighted aspects using a raster calculator. It is classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The accuracy and performance of the models were evaluated using the area under the curve (AUC). As the result indicated, the FR model has better performance (AUC = 99.1%) compared to the AHP model (AUC = 86.9%), LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This research finds out that the applied methods are quite worthy for flood susceptibility modeling within the study area. In flood susceptibility modeling, method selection is not a serious challenge; the care should tend to the input parameter quality. Based on the AUC values, the FR model is comparatively better, followed by the AHP model for regional land use planning, flood hazard mitigation, and prevention purposes.

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