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.

This 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.

Geographic Information Systems for Geoscientists: Modelling with GIS provides an introduction to the ideas and practice of GIS to students and professionals from a variety of geoscience backgrounds. The emphasis in the book is to show how spatial data from various sources (principally paper maps, digital images and tabular data from point samples) can be captured in a GIS database, manipulated, and transformed to extract particular features in the data, and combined together to produce new derived maps, that are useful for decision-making and for understanding spatial interrelationship. The book begins by defining the meaning, purpose, and functions of GIS. It then illustrates a typical GIS application. Subsequent chapters discuss methods for organizing spatial data in a GIS; data input and data visualization; transformation of spatial data from one data structure to another; and the combination, analysis, and modeling of maps in both raster and vector formats. This book is intended as both a textbook for a course on GIS, and also for those professional geoscientists who wish to understand something about the subject. Readers with a mathematical bent will get more out of the later chapters, but relatively non-numerate individuals will understand the general purpose and approach, and will be able to apply methods of map modeling to clearly-defined problems.

This book presents landslide studies using the geographic information system (GIS), which includes not only the science of GIS and remote sensing, but also technical innovations, such as detailed light detection and ranging profiles, among others. To date most of the research on landslides has been found in journals on topography, geology, geo-technology, landslides, and GIS, and is limited to specific scientific aspects. Although journal articles on GIS using landslide studies are abundant, there are very few books on this topic. This book is designed to fill that gap and show how the latest GIS technology can contribute in terms of landslide studies. In a related development, the GIS Landslide Workshop was established in Japan 7 years ago in order to communicate and solve the scientific as well as technical problems of GIS analyses, such as how to use GIS software and its functions. The workshop has significantly contributed to progress in the field. Included among the chapters of this book are GIS using susceptibility mapping, analyses of deep-seated and shallow landslides, measuring and visualization of landslide distribution in relation to topography, geological facies and structures, rivers, land use, and infrastructures such as roads and streets. Filled with photographs, figures, and tables, this book is of great value to researchers in the fields of geography, geology, seismology, environment, remote sensing, and atmospheric research, as well as to students in these fields.

With the increasing need to take an holistic view of landslide hazard and risk, this book overviews the concept of risk research and addresses the sociological and psychological issues resulting from landslides. Its integrated approach offers understanding and ability for concerned organisations, landowners, land managers, insurance companies and researchers to develop risk management solutions. Global case studies illustrate a variety of integrated approaches, and a concluding section provides specifications and contexts for the next generation of process models.

Terrestrial mass movements (i.e. cliff collapses, soil creeps, mudflows, landslides etc.) are severe forms of natural disasters mostly occurring in mountainous terrain, which is subjected to specific geological, geomorphological and climatological conditions, as well as to human activities. It is a challenging task to accurately define the position, type and activity of mass movements for the purpose of creating inventory records and potential vulnerability maps. Remote sensing techniques, in combination with Geographic Information System tools, allow state-of-the-art investigation of the degree of potential mass movements and modeling surface processes for hazard and risk mapping. Similarly, through statistical prediction models, future mass-movement-prone areas can be identified and damages can to a certain extent be minimized. Issues of scale and selection of morphological attributes for the scientific analysis of mass movements call for new developments in data modeling and spatio-temporal GIS analysis. The book is a product of a cooperation between the editors and several contributing authors, addressing current issues and recent developments in GI technology and mass movements research. Its fundamental treatment of this technology includes data modeling, topography, geology, geomorphology, remote sensing, artificial neural networks, binomial regression, fuzzy logic, spatial statistics and analysis, and scientific visualization. Both theoretical and practical issues are addressed.