Landslide Susceptibility Modeling

Landslide Susceptibility Modeling PDF

Author: Hiromitsu Yamagishi

Publisher: Elsevier

Published: 2023-01-15

Total Pages: 300

ISBN-13: 9780128235058

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In Landslide Susceptibility Mapping Paradigm: A Practical Guide for Landslide Susceptibility and Hazard Assessments expert researchers detail how remote sensing and GIS are now being used in landslide susceptibility mapping. Landslides are a significant environmental problem in many countries. The destructive outcomes of landslides in relation to life and property are severe and much higher than any other natural disaster. Since the causes of landslides are obscure and are beyond human control, it is practically impossible to prevent landslides. The task before the researchers, planner and administrator, therefore lies with the identification of hazard-prone areas and their zonation in different scales for preparedness and mitigation. Each chapter in Landslide Susceptibility Mapping Paradigm describes a methodology that has been successfully used in the past and can therefore be applied for mapping landslide susceptibility. With the sample data provided along with the publication and several case study examples of different models of landslide susceptibility maps, researchers across the earth sciences as well as those working in hazard and disaster preparedness will have the tools to create susceptibility maps for specific locales. Address the key issues and challenges in landslide inventory mapping Features step-by-step guidelines in landslide susceptibility modeling, from traditional methods to advanced machine learning techniques Companion website includes data and codes used in the book as well as video tutorials

Geoinformatics and Modelling of Landslide Susceptibility and Risk

Geoinformatics and Modelling of Landslide Susceptibility and Risk PDF

Author: Sujit Mandal

Publisher: Springer

Published: 2019-05-28

Total Pages: 223

ISBN-13: 3030104958

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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.

Spatial Modeling in GIS and R for Earth and Environmental Sciences

Spatial Modeling in GIS and R for Earth and Environmental Sciences PDF

Author: Hamid Reza Pourghasemi

Publisher: Elsevier

Published: 2019-01-18

Total Pages: 798

ISBN-13: 0128156953

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Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling. Offers a clear, interdisciplinary guide to serve researchers in a variety of fields, including hazards, land surveying, remote sensing, cartography, geophysics, geology, natural resources, environment and geography Provides an overview, methods and case studies for each application Expresses concepts and methods at an appropriate level for both students and new users to learn by example

Laser Scanning Applications in Landslide Assessment

Laser Scanning Applications in Landslide Assessment PDF

Author: Biswajeet Pradhan

Publisher: Springer

Published: 2017-05-04

Total Pages: 359

ISBN-13: 3319553429

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This book is related to various applications of laser scanning in landslide assessment. Landslide detection approaches, susceptibility, hazard, vulnerability assessment and various modeling techniques are presented. Optimization of landslide conditioning parameters and use of heuristic, statistical, data mining approaches, their advantages and their relationship with landslide risk assessment are discussed in detail. The book contains scanning data in tropical forests; its indicators, assessment, modeling and implementation. Additionally, debris flow modeling and analysis including source of debris flow identification and rockfall hazard assessment are also presented.

Landslide Hazard and Risk

Landslide Hazard and Risk PDF

Author: Thomas Glade

Publisher: John Wiley & Sons

Published: 2006-01-04

Total Pages: 824

ISBN-13: 0470012641

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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.

Statistical Approaches for Landslide Susceptibility Assessment and Prediction

Statistical Approaches for Landslide Susceptibility Assessment and Prediction PDF

Author: Sujit Mandal

Publisher: Springer

Published: 2018-09-03

Total Pages: 193

ISBN-13: 3319938975

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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.

Feature Engineering for Machine Learning

Feature Engineering for Machine Learning PDF

Author: Alice Zheng

Publisher: "O'Reilly Media, Inc."

Published: 2018-03-23

Total Pages: 218

ISBN-13: 1491953195

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Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

Landslide: Susceptibility, Risk Assessment and Sustainability

Landslide: Susceptibility, Risk Assessment and Sustainability PDF

Author: Gopal Krishna Panda

Publisher: Springer

Published: 2024-06-13

Total Pages: 0

ISBN-13: 9783031565908

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The book illustrates a geospatial and geostatistical approach to data analysis, modeling, risk assessment, and visualization, as well as landslide hazard management in the hilly region. This book investigates cutting-edge methodologies based on open source software and R statistical programming and modeling in current decision-making procedures, with a particular emphasis on recent advances in data mining techniques and robust modeling in torrential rainfall and earthquake induced landslide hazard.

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