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Predictive Models for the Development of Landslide Early Warning Systems - Softcover

 
9780323959292: Predictive Models for the Development of Landslide Early Warning Systems
Softcover
ISBN 10: 0323959296 ISBN 13: 9780323959292
Publisher: Elsevier, 2029

Synopsis

Predictive Models for the Development of Landslide Early Warning Systems details advanced techniques in implementing landslide early warning systems (LEWS). The book provides a comprehensive resource for practitioners by including different techniques and models in landslide early warning, their practical applications, and case studies. The modeling theory is provided in a detailed but succinct format, verified with onsite models for specific regions and scenarios for different types of landslides and triggering factors. The book covers four main topics, including monitoring, data acquisition, transmission and maintenance of the instruments; analysis and forecasting, forecasting methods, and warning/dissemination of understandable messages alerting.

The exportability of different models is discussed in detail and followed by practical demonstrations for expert researchers’ as well as postgraduates’ needs. The book offers in-depth, up-to-date best practices for implementing LEWS based on current effective systems, new technologies, and standard methodologies at global level.

  • Presents advanced computational techniques in developing and implementing landslide early warning systems
  • Details landslide early warning systems for various types of landslides and at different regional scales, allowing readers to quickly correlate the theory and practical applications covered into real-world solutions
  • Provides case studies of different landslide types and at different levels, from local to national scale
  • Includes thorough details on the application of different ‘Internet of Things’ based instruments for slope monitoring at different scales

"synopsis" may belong to another edition of this title.

About the Authors

Professor Pradhan is a globally recognized expert in geospatial analytics and artificial intelligence applications in Earth and environmental sciences. Currently a Distinguished Professor at the University of Technology Sydney (UTS), Australia, he also leads the Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS). With a PhD in GIS-based modeling, Prof. Pradhan has over two decades of experience in spatial data science, remote sensing, natural hazard modeling, and environmental monitoring. He has been listed among the world's top 2% scientists by Stanford University and received numerous international awards, including from IEEE and Elsevier. A Fellow of the Royal Geographical Society (FRGS), he also serves on editorial boards of several top-tier journals. His research integrates geospatial AI and deep learning for disaster risk reduction, land use planning, and sustainability.

Dr Neelima Satyam is currently Associate Professor and Head of the Department of Civil Engineering at IIT Indore. She is actively engaged in teaching, research and consultancy in the field of Geotechnical engineering.

Dr. Neelima has published over 150 papers in reputed journals and conferences. She is the Co-opted member of PAC Civil and Mechanical Engineering SERB, DST (2015-2018). She has been the Chairperson of the selection committee for MEXT Scholarships of Japan since 2015. Dr Satyam is a recipient of IEI Young Engineers Award 2011; BRNS Young Scientist Research Award 2011; AICTE Career award 2012; JSPS fellowship in 2013; Young Woman Engineer award from INWES in 2012 and CIDC Vishwakarma Award in 2021

From the Back Cover

Predictive Models for the Development of Landslide Early Warning Systems details advanced techniques in implementing landslide early warning systems (LEWS). It provides a comprehensive resource for practitioners including different techniques and models in landslide early warning, their practical applications, and case studies. The modelling theory is provided in a detailed but succinct format, verified with on-site models for specific regions and scenarios for different types of landslides and triggering factors. The book covers four main topics required for an efficient LEWS: monitoring, including data acquisition, transmission and maintenance of the instruments; analysis and forecasting, including forecasting methods; warning/dissemination of understandable messages alerting for the impending threat; and response. The exportability of different models is discussed in detail and followed by practical demonstrations for expert researchers’ as well as postgraduates’ needs.

Predictive Models for the Development of Landslide Early Warning Systems offers in-depth, up-to-date best practices for implementing LEWS, based on current effective systems, new technologies, and standard methodologies at global level. This allows readers to understand universal LEWS methodologies and applications for use in their own research and development, including the use of current practical Big Data techniques.

"About this title" may belong to another edition of this title.

Publisher
Elsevier
Publication date
2029
Language
English
ISBN 10 
0323959296
ISBN 13 
9780323959292
Binding
Paperback
Edition number
1
Number of pages
250
Editor
Pradhan Biswajeet, Satyam Neelima