Feature-Based Landslide Susceptibility and Hazard Zonation Maps using Fuzzy Overlay Analysis

2020 ◽  
Author(s):  
Litesh Bopche ◽  
Priti P. Rege
Keyword(s):  
Landslide Susceptibility ◽  
Overlay Analysis ◽  
Hazard Zonation ◽  
Feature Based ◽  
Zonation Maps

scholarly journals Landforms and rural development: The evolution of human settlements at alpine alluvial fans in the Admont Valley, Austria, since 1784 AD

2019 ◽  
Vol 21 (1) ◽  
pp. 29-43
Author(s):  
Andreas KELLERER-PIRKLBAUER ◽  
Julia EULENSTEIN
Keyword(s):  
Eastern Europe ◽  
Large Scale ◽  
Central And Eastern Europe ◽  
Alluvial Fans ◽  
Spatial Analyses ◽  
Hazard Zonation ◽  
Risk Zones ◽  
Zonation Maps ◽  
Francis I ◽  
Insight Into

We used two historical maps that cover vast areas of central and eastern Europe at rather large scale dating to 1784 (First Military Survey of the Habsburg Empire; total extent 640,000 km²; scale 1: 28,800) and 1824 (cadastral land register of Francis I; 670,000 km²; 1: 2,880) to extracted individual buildings located at several alluvial fans in one valley in Austria (Admont Valley). Historic buildings were mapped and compared with present building (airborne–laserscanning based; 2008–2017), geomorphic (landform distribution), geomorphodynamic (documented damaging events at torrents), and spatial planning (hazard zonation maps) data. Results show that 69.2% of all present buildings are located at only 7% of the study area. Whereas the 1784–data are too inaccurate and unprecise for detailed spatial analyses, the 1824–data are very accurate and precise allowing spatial and socio–economic insight into the population and building evolution over a 190–year period. Results show for instance that despite a tremendous increase in buildings (911 in 1824; 3554 in 2008–2017), the proportion of buildings exposed to torrents–related natural hazards significantly decreased by 10.4% for yellow (moderate–risk) and by 13.7% for red (high–risk) zones. Similar historio–geomorphological studies as presented here might be accomplished in other countries in central and eastern Europe covered by the indicated historical map products.

scholarly journals GIS based Landslides Susceptibility Mapping using Probabilistic Certainty Factor for Hazard Zonation in Mandakini Valley

2020 ◽  
Vol 8 (6) ◽  
pp. 398-404
Keyword(s):  
High Risk ◽  
Landslide Susceptibility ◽  
Catchment Area ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Hazard Zonation ◽  
Certainty Factor ◽  
Landslide Susceptibility Map ◽  
Risk Zonation

Landslides are highly threatening a phenomenon which is very common in hilly region and mountainous regions. These landslides trigger major risks leading to heavy losses in terms of life and property. Many studies were conducted globally to determine Landslide vulnerability of different locations. In order to assess vulnerability, there were few studies around Landslides Susceptibility mapping also whose main objective is to identify high-risk vulnerable areas, there by applying measure to reduce the damage caused, if it were to happen in near future. In literature, there are many methods available for predictive susceptibility mapping of landslides. However, identification of any of the prevalent method for a specific area require utmost care and prudence because land sliding is a result of complex geo-environmental spatial factors. Mandakini valley is highly ruggedized terrain with intensive rains during monsoon season. As a result, Landslides are very common in the Mandakini River valley and its catchment area. These landslides cause severe damage to human settlements and infrastructure present in this area. In this study, we have used certainty factor method in order to generate landslide susceptibility map for the catchment area of Mandakini river. Certainty factor approach is a bi-variate probabilistic method which uses Geo-environmental parameters like elevation, slope, aspect, rainfall distance away from river, soil characteristics etc. to generate landslide susceptibility map. A Script was developed in ArcPy - a python package to design tools for generating susceptibility map. These tools can run both at desktop level and at server level and generate results in an integrated way. Esri ArcMap 10.7 is used in order to generate required data layers and thematic maps. Overall, this paper leverages GIS technology and its tools to performs Landslide Susceptibility Mapping using Probabilistic Certainty Factor and generate Hazard Zonation of Mandakini Valley using an automated script for generating Landslide Susceptibility Mapping and Hazard Risk Zonation. It was found that out of 696, total 136 villages are under high risk of landsides, total 329 villages are under moderate risks and around 231 villages are under low risk zonation impacting lives of approx. 216166 people. Also, it is worth mentioning that a GIS based script was developed to automate generation of Landslide Susceptibility Maps which can be used where the same geological and topographical feature prevails.

Space-Time Integrated Landslide Hazard Zonation near Tehri Dam in Uttarakhand, India

2017 ◽  
pp. 122-146
Author(s):  
Pooja Rana ◽  
Jeganathan Chockalingam ◽  
Arvind Chandra Pandey
Keyword(s):  
Factor Model ◽  
High Accuracy ◽  
Landslide Hazard ◽  
Western Himalayas ◽  
Hazard Zonation ◽  
Bivariate Model ◽  
Zonation Maps ◽  
Tehri Dam ◽  
Factor Maps ◽  
Multiple Factor

The study aims to predict landslide hazard zones near Tehri Dam in Uttarakhand State located in the Western Himalayas in India. Four different models were analysed: Weight Factor Model (M1), Multiple Factor Model (M2), Statistical Bivariate model (M3) and Analytical Hierarchical Processes (AHP) model (M4). Five different combination of reference landslides were used for deriving weights of the classes in the factor maps: all the landslides from 1990, 2002 & 2010 (C1); landslides from 2010 (C2); landslides from 1990 & 2002 (C3); landslides located within 500m from roads (C4); landslides located beyond 500m from roads (C5). The accuracy resulted from each model in each combination was [Mn:C1, C2…Cn]: M1: 60,44,46,38,66%; M2:70,76,79,73,71%; M3:45,37,23,36,85%; M4:64,51,51,64,36%. Multiple Factor Model (M2) resulted in a consistently high accuracy in all the combinations. Finally, the 20 different model outputs were integrated to derive unified hazard zonation maps based on: (a) mean (85% accuracy), (b) penalisation (57% accuracy) and (c) k-means cluster (80% accuracy) approaches.

The Comparison Between Certainty Factor Method and Index of Entropy Method For Landslide Hazard Assessment: South Western Chamba District, Himachal Pradesh, India

2021 ◽  
Author(s):  
Desh Deepak Pandey ◽  
Rajeshwar Singh Banshtu ◽  
Ambrish Kumar Mahajan ◽  
Laxmi Devi Versain
Keyword(s):  
Land Use ◽  
Entropy Method ◽  
Landslide Hazard ◽  
Hazard Zonation ◽  
Landslide Hazard Zonation ◽  
Certainty Factor ◽  
Factor Method ◽  
Index Of Entropy ◽  
Causative Factors ◽  
Zonation Maps

Abstract The present study reflects the contributions of geo-environmental factors that were analyzed for the development of landslide hazard zonation map using certainty factor method and index of entropy method. Heavy rainfall, unscientific excavation of slopes during road construction, expansion of infrastructure, and unplanned growth in urban population were the major factors for unstable slopes in the Lesser Himalayan region. Historical database, interpretation of satellite and Google earth images were used to identification of 248 landslides. The data collected using remote sensing images have been verified by conducting ground truth surveys undertaken from January 2018 to October 2020 in preparing the landslide inventory of the study area. Inventory thus generated was divided into 70% training and 30% validation datasets. Relationships between slope failure and its causative factors (relief, slope, aspect, curvature, lithology, soil, weathering, land use, lineament density, rainfall, and density of drainage networks) were analyzed by using certainty factor (CF) and index of entropy (IOE) methods. The analysis of all causative factors and assigning relative weightage values by using the index of entropy and certainty factor models leads to the generation of Landslide hazard zonation maps of the region. Finally, the landslide prediction accuracy of hazard zonation maps was calculated by drawing Successive Rate Curve (SRC) curves for both training and validation datasets. The outcomes of this study will be useful to government agencies, planners, decision-makers, researchers, and general land-use planners for sustainable development of the study area.

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