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.
Table of Contents
1. Landslide Inventory and landslide susceptibility modelling2. Empirical and statistical rainfall thresholds
3. Seismically induced landslides: reconstruction and forecasting
4. Process based slope stability modelling
5. Monitoring of unstable slopes and wireless communication
6. Data processing techniques in landslide research regarding rainfall threshold definition, reconstruction and near real time forecasting of seismic data
7. Numerical modelling of debris flows, mud flows and rockfalls
8. Social and Economic Impact, and Policies of landslide early warning systems
9. LEWS: Technological advancements and future scope
Authors
Biswajeet Pradhan Distinguished Professor and Director, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, School of Information, Systems and Modelling; Faculty of Engineering and IT, New South Wales, Australia. Biswajeet Pradhan is a distinguished professor at UTS School of Civil and Environmental Engineering. He is an international expert in data-driven modelling and a pioneer in combining spatial modelling with statistical and machine learning models for natural hazard predictions including landslides. He has a track record of outstanding research outputs, with over 600 journal articles. He is a highly interdisciplinary researcher with publications across 12 areas, listed as having 'Excellent' international collaboration status. He has been a Highly Cited Researcher for five consecutive years (2016-2020) and ranks fifth in the field of Geological & Geoenvironmental Engineering. Neelima Satyam Associate Professor and Head, Department of Civil Engineering, IIT Indore, India. 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
