rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical Approaches for Landslide Susceptibility Assessment and Prediction
  name:
    name part: Mandal, Sujit.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Mondal, Subrata.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 193 p. 66 illus., 61 illus. in color. online resource.
  abstract: This book focuses on the spatial distribution of landslide hazards of the Darjeeling Himalayas. Knowledge driven methods and statistical techniques such as frequency ratio model (FRM), information value model (IVM), logistic regression model (LRM), index overlay model (IOM), certainty factor model (CFM), analytical hierarchy process (AHP), artificial neural network model (ANN), and fuzzy logic have been adopted to identify landslide susceptibility. In addition, a comparison between various statistical models were made using success rate cure (SRC) and it was found that artificial neural network model (ANN), certainty factor model (CFM) and frequency ratio based fuzzy logic approach are the most reliable statistical techniques in the assessment and prediction of landslide susceptibility in the Darjeeling Himalayas. The study identified very high, high, moderate, low and very low landslide susceptibility locations to take site-specific management options as well as to ensure developmental activities in theDarjeeling Himalayas. Particular attention is given to the assessment of various geomorphic, geotectonic and geohydrologic attributes that help to understand the role of different factors and corresponding classes in landslides, to apply different models, and to monitor and predict landslides. The use of various statistical and physical models to estimate landslide susceptibility is also discussed. The causes, mechanisms and types of landslides and their destructive character are elaborated in the book. Researchers interested in applying statistical tools for hazard zonation purposes will find the book appealing.
  table of contents: Concept on Landslides and Landslide Susceptibility -- Geomorphic, Geo-tectonic and Hydrologic Attributes and Landslide Probability -- Frequency Ratio Model (FRM) and Information Value Model (IVM) in landslide susceptibility Assessment and Prediction -- Logistic Regression Model (LRM) and Landslide Susceptibility: A RS & GIS based approach -- Artificial Neural Network (ANN) Model and Landslide Susceptibility -- Weighted Index Overlay Model (WIOM), Certainty factor approach (CFA) and Analytical Hierarchy Process (AHP) in Landslide Susceptibility Studies -- Knowledge driven Statistical Approach for Landslide Susceptibility Assessment using GIS and Fuzzy Logic -- Comparison between Statistical Models: A Review and Evaluation -- Index.
  note: by Sujit Mandal, Subrata Mondal.
  subject:
    topic: Natural disasters
  subject:
    topic: Statistics
  subject:
    topic: Physical geography
  subject:
    topic: Hydrogeology
  subject:
    topic: Geoecology
  subject:
    topic: Environmental geology
  subject:
    topic: Natural Hazards
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Physical Geography
  subject:
    topic: Hydrogeology
  subject:
    topic: Geoecology/Natural Processes
  classification: GB5000-5030
  classification: 551
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319938974
  identifier: https://doi.org/10.1007/978-3-319-93897-4
  record info:
    record creation date: 180903
    record change date: 20191027022940.0
    record identifier: 978-3-319-93897-4