Landslide Susceptibility Analysis: A Case Study of Nainital Municipal Area

Abstract Landslides are one of the most recurrent natural phenomena that are of overwhelming significance in the Himalayas. The Himalayan terrain being under severe transmutation by human interference and excess urban penetration has led to triggering of landslides along with causing colossal damage to property and loss of life. Immense risk looms large all along the Himalayas with cumulating conditions that build the potentiality to landslides. The study of landslides has drawn worldwide attention mainly due to the aggravating socio-economic consequences as well as the increasing pressure of urbanization on the mountain environment. In order to reduce the damage and manage vulnerable areas, there is imperative need to formulate comprehensive Landslide Vulnerability and Susceptibility Zonation maps for different areas of the Himalayan region emphasizing the urbanized and burgeoning pockets. The concept of landslide susceptibility and landslide susceptibility assessment have been introduced in the past couple of decades and various methodologies have been developed for evaluating the devastating power of landslides and its associated processes. The ultimate aim is to evolve a method suitable for specific areas through which appropriate management measures can be taken to reduce the risk from potential landslides. Any approach towards LSZ would require identification of the conditions leading to slope failure, their systematic mapping and evaluation of their relative contributions by amalgamation of all factors in the ultimatum. The aim of this paper is to assess the various landslide vulnerability factors in Nainital Municipality area on raster-based GIS platform and generate landslide vulnerability and susceptibility maps. To achieve the objective, a detailed inventory of maps based on all parameters assessed has been generated of the study area from the satellite imageries and field data. The accuracy of results is being validated by constant observation and prediction accuracies.

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Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya

Environmental Earth Sciences ◽

10.1007/s12665-009-0426-5 ◽

2010 ◽

Vol 61 (5) ◽

pp. 1049-1064 ◽

Cited By ~ 136

Author(s):

Chandra Prakash Poudyal ◽

Chandong Chang ◽

Hyun-Joo Oh ◽

Saro Lee

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Nepal Himalaya ◽

Susceptibility Maps ◽

Artificial Neural ◽

Landslide Susceptibility Maps

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A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey

Landslides ◽

10.1007/s10346-011-0283-7 ◽

2011 ◽

Vol 9 (1) ◽

pp. 93-106 ◽

Cited By ~ 254

Author(s):

Aykut Akgun

Keyword(s):

Logistic Regression ◽

Likelihood Ratio ◽

Landslide Susceptibility ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

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Strategies investigation in using artificial neural network for landslide susceptibility mapping: application to a Sicilian catchment

Journal of Hydroinformatics ◽

10.2166/hydro.2013.191 ◽

2013 ◽

Vol 16 (2) ◽

pp. 502-515 ◽

Cited By ~ 16

Author(s):

Elisa Arnone ◽

Antonio Francipane ◽

Leonardo V. Noto ◽

Antonino Scarbaci ◽

Goffredo La Loggia

Keyword(s):

Landslide Susceptibility ◽

Slope Failure ◽

Hazard Analysis ◽

Physical Phenomenon ◽

Sample Selection ◽

Landslide Susceptibility Mapping ◽

Susceptibility Maps ◽

Definition Of ◽

Basin Area ◽

Small Basin

Susceptibility assessment of areas prone to landsliding remains one of the most useful approaches in landslide hazard analysis. The key point of such analysis is the correlation between the physical phenomenon and its triggering factors based on past observations. Many methods have been developed in the scientific literature to capture and model this correlation, usually within a geographic information system (GIS) framework. Among these, the use of neural networks, in particular the multi-layer perceptron (MLP) networks, has provided successful results. A successful application of the MLP method to a basin area requires the definition of different model strategies, such as the sample selection for the training phase or the design of the network structure. The present study investigates the effects of these strategies on the development of landslide susceptibility maps by applying different model configurations to a small basin located in northeastern Sicily (Italy), where a number of historical slope failure events have been documented over the years. Model performances and their comparison are evaluated using specific metrics.

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The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan

Geoenvironmental Disasters ◽

10.1186/s40677-019-0137-5 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Kounghoon Nam ◽

Fawu Wang

Keyword(s):

Landslide Susceptibility ◽

Assessment Methods ◽

Disaster Mitigation ◽

Susceptibility Assessment ◽

Ground Acceleration ◽

Susceptibility Maps ◽

Landslide Susceptibility Index ◽

High Possibility ◽

Northern Japan

Abstract Background Thousands of landslides were triggered by the Hokkaido Eastern Iburi earthquake on 6 September 2018 in Iburi regions of Hokkaido, Northern Japan. Most of the landslides (5627 points) occurred intensively between the epicenter and the station that recorded the highest peak ground acceleration. Hundreds of aftershocks followed the major shocks. Moreover, in Iburi region, there is a high possibility of earthquakes occurring in the future. Effective prediction and susceptibility assessment methods are required for sustainable management and disaster mitigation in the study area. The aim of this study is to evaluate the performance of an autoencoder framework based on deep neural network for prediction and susceptibility assessment of regional landslides triggered by earthquakes. Results By applying 12 sampling sizes and 12 landslide-influencing factors, 12 landslide susceptibility maps were produced using an autoencoder framework. The results of the model were evaluated using qualitative and quantitative assessment methods. The ratios of the sampling sizes on the non-landslide points randomly generated from the combination zone including plain and mountain (PM) and a mountainous only zone (M) affected different prediction abilities of the model’s performance. Conclusions The 12 susceptibility maps, including the landslide susceptibility index, indicated the various spatial distributions of the landslide susceptibility values in both PM and the M. The highly accurate models explicitly distinguished the potential areas of landslide from stable areas without expanding the spatial extent of the potential landslide areas. The autoencoder is proved to be an effective and efficient method for extracting spatial patterns through unsupervised learning for the prediction and susceptibility assessment of landslide areas.

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Validation of Landslide Susceptibility Maps; Examples and Applications from a Case Study in Northern Spain

Natural Hazards ◽

10.1023/b:nhaz.0000007201.80743.fc ◽

2003 ◽

Vol 30 (3) ◽

pp. 437-449 ◽

Cited By ~ 150

Author(s):

Juan Remondo ◽

Alberto González ◽

José Ramón Díaz De Terán ◽

Antonio Cendrero ◽

Andrea Fabbri ◽

...

Keyword(s):

Landslide Susceptibility ◽

Northern Spain ◽

Susceptibility Maps ◽

Landslide Susceptibility Maps

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Landslide Susceptibility Modelling for Agricultural activities in Hilly Areas

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d5139.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 6206-6212

Keyword(s):

Landslide Susceptibility ◽

Slope Failure ◽

Regional Scale ◽

Susceptibility Map ◽

Detailed Knowledge ◽

Susceptibility Maps ◽

Susceptibility Factors ◽

Cameron Highlands ◽

Probable Failure ◽

Made In

The slope failure risk assessment of a particular area can be prepared by considering the data available. Many attempts have been made to classify the risk where evaluations are made in rating or in grading the slopes based on their characteristics and erosion problems. The assessments were done for geo-hazard such as erosion and landslide recognized in planning and guidance. Most of the hazard risk analyses require detailed knowledge of the geo- environmental predisposition factors and initial events that led to failure. The results of these analyses consist of identification and mapping of all erosion induced landslide phenomenon and are often translated in the form of maps, which is the fundamental step of the hazard assessment. The ranking of susceptibility areas and the delineation of probable failure areas are among essential features relevant to the production of these maps. In this study, Landslide Susceptibility Modelling was developed by taking into consideration all the landslide susceptibility factors in Cameron Highlands. The landslide susceptibility map was produced based on the historical records of a landslide in that area for 20 years and the frequency ratio model was developed using mapoverlaying techniques. The susceptibility map offers substantial benefits as a regional-scale tool over earlier susceptibility maps and Cameron Highland landslide- susceptible terrain zoning. The susceptibility map has the advantage of assisting with the implementation of suitable efforts to prevent landslides.

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