Pradhan / Satyam | Predictive Models for the Development of Landslide Early Warning Systems | Buch | 978-0-323-95929-2 | sack.de

Buch, Englisch, 250 Seiten, Format (B × H): 152 mm x 229 mm

Pradhan / Satyam

Predictive Models for the Development of Landslide Early Warning Systems


Erscheinungsjahr 2023
ISBN: 978-0-323-95929-2
Verlag: Elsevier Science

Buch, Englisch, 250 Seiten, Format (B × H): 152 mm x 229 mm

ISBN: 978-0-323-95929-2
Verlag: Elsevier Science


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.

Pradhan / Satyam Predictive Models for the Development of Landslide Early Warning Systems jetzt bestellen!

Weitere Infos & Material


1. Landslide Inventory and landslide susceptibility modelling2. Empirical and statistical rainfall thresholds3. Seismically induced landslides: reconstruction and forecasting 4. Process based slope stability modelling5. Monitoring of unstable slopes and wireless communication6. Data processing techniques in landslide research regarding rainfall threshold definition, reconstruction and near real time forecasting of seismic data7. Numerical modelling of debris flows, mud flows and rockfalls8. Social and Economic Impact, and Policies of landslide early warning systems9. LEWS: Technological advancements and future scope


Pradhan, Biswajeet
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.

Satyam, Neelima
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



Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.