|
![]() |
|||
|
||||
OverviewThis book discusses various statistical models and their implications for developing landslide susceptibility and risk zonation maps. It also presents a range of statistical techniques, i.e. bivariate and multivariate statistical models and machine learning models, as well as multi-criteria evaluation, pseudo-quantitative and probabilistic approaches. As such, it provides methods and techniques for RS & GIS-based models in spatial distribution for all those engaged in the preparation and development of projects, research, training courses and postgraduate studies. Further, the book offers a valuable resource for students using RS & GIS techniques in their studies. Full Product DetailsAuthor: Sujit Mandal , Subrata MondalPublisher: Springer Nature Switzerland AG Imprint: Springer Nature Switzerland AG Edition: 1st ed. 2019 Weight: 0.530kg ISBN: 9783030104948ISBN 10: 303010494 Pages: 223 Publication Date: 11 June 2019 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand ![]() We will order this item for you from a manufactured on demand supplier. Table of ContentsLandslides: An Overview.- Geomorphic, Geo-tectonic and Hydrologic Attributes and Landslide Susceptibility.- Slope instability analysis: A Sub-Watershed Scale Study.- Geomorphic Diversity and Landslide Susceptibility: A Multi-Criteria Evaluation Approach.- Landslide Susceptibility Prediction and Assessment using Bivariate Models.- Multivariate Models and Prediction and Assessment of Landslide Susceptibility.- Application of Probabilistic Approach in Landslide Susceptibility Analysis.- Machine Learning Models in Landslides Susceptibility Assessment.- Factor and Factors’ Cluster Analysis and Landslide Susceptibility Assessment.- Micro level Landslide Susceptibility and Risk Assessment.- Comparison between Susceptibility Models: A critical Review and Evaluation.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |