Understanding and Reducing Landslide Disaster Risk
Understanding and Reducing Landslide Disaster Risk

Author: Nicola Casagli

Publisher:

Published: 2021

Total Pages: 361

ISBN-13: 9783030603120

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This book is a part of ICL new book series "ICL Contribution to Landslide Disaster Risk Reduction" founded in 2019. Peer-reviewed papers submitted to the Fifth World Landslide Forum were published in six volumes of this book series. This book contains the followings: " One theme lecture and one keynote lecture " Monitoring and remote sensing for landslide risk mitigation, including one keynote lecture " Landslide early warning systems, forecasting models and time prediction of landslides Prof. Nicola Casagli is a Vice President and President-elect of the International Consortium on Landslides (ICL) for 2021-2023. He is Professor of engineering geology at the Department of Earth Sciences, University of Florence, and President of the National Institute of Oceanography and Applied Geophysics - OGS, Trieste, Italy. Dr. Veronica Tofani is an Associate Professor at the Department of Earth Sciences, University of Florence, and Program Coordinator of the UNESCO Chair on Prevention and Sustainable Management of Geo-hydrological hazards, University of Florence. Prof. Kyoji Sassa is the Founding President and the Secretary-General of the International Consortium on Landslides (ICL). He has been the Editor-in-Chief of International Journal Landslides since its foundation in 2004. Prof. Peter Bobrowsky is the President of the International Consortium on Landslides. He is a Senior Scientist of Geological Survey of Canada, Ottawa, Canada. Prof. Kaoru Takara is the Executive Director of the International Consortium on Landslides. He is a Professor and Dean of Graduate School of Advanced Integrated Studies (GSAIS) in Human Survivability (Shishu-Kan), Kyoto University

Landslide Analysis and Early Warning Systems
Landslide Analysis and Early Warning Systems

Author: Benni Thiebes

Publisher: Springer Science & Business Media

Published: 2012-01-21

Total Pages: 272

ISBN-13: 3642275265

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Recent landslide events demonstrate the need to improve landslide forecasting and early warning capabilities in order to reduce related risks and protect human lives. In this thesis, local and regional investigations were carried out to analyse landslide characteristics in the Swabian Alb region, and to develop prototypic landslide early warning systems. In the local study area, an extensive hydrological and slope movement monitoring system was installed on a seasonally reactivated landslide body located in Lichtenstein- Unterhausen. Monitoring data was analysed to assess the influence of rainfall and snow-melt on groundwater conditions, and the initiation of slope movements. The coupled hydrology-slope stability model CHASM was applied to detect areas most prone to slope failures, and to simulate slope stability using a variety of input data. Subsequently, CHASM was refined and two web-based applications were developed: a technical early warning system to constantly simulate slope stability integrating rainfall measurements, hydrological monitoring data and weather forecasts; and a decision-support system allowing for quick calculation of stability for freely selectable slope profiles. On the regional scale, available landslide inventory data were analysed for their use in evaluation of rainfall thresholds proposed in other studies. Adequate landslide events were selected and their triggering rainfall and snow-melting conditions were compared to intensity-duration and cumulative thresholds. Based on the results, a regional landslide early warning system was developed and implemented as a webbased application. Both, the local and the regional landslide early warning systems are part of a holistic and integrative early warning chain developed by the ILEWS project, and could easily be transferred to other landslide prone areas.

Landslides - Disaster Risk Reduction
Landslides - Disaster Risk Reduction

Author: Kyoji Sassa

Publisher: Springer Science & Business Media

Published: 2008-10-27

Total Pages: 638

ISBN-13: 354069966X

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This book documents the First World Landslide Forum, which was jointly organized by the International Consortium on Landslides (ICL), eight UN organizations (UNESCO, WMO, FAO, UN/ISDR, UNU, UNEP, World Bank, UNDP) and four NGOs (International Council for Science, World Federation of Engineering Organizations, Kyoto Univ. and Japan Landslide Society) in Tokyo in 2008. The material consists of four parts: The Open Forum "Progress of IPL Activities; Four Thematic Lectures in the Plenary Symposium "Global Landslide Risk Reduction"; Six Keynote Lectures in the Plenary session; and the aims and overviews of eighteen parallel sessions (dealing with various aspects necessary for landslide disaster risk reduction such as: observations from space; climate change and slope instability; landslides threatening heritage sites; the economic and social impact of landslides; monitoring, prediction and early warning; and risk-management strategies in urban area, etc.) Thus it enables the reader to benefit from a wide range of research intended to reduce risk due to landslide disasters as presented in the first global multi-disciplinary meeting.

A Safer Future
A Safer Future

Author: National Research Council

Publisher: National Academies Press

Published: 1991-02-01

Total Pages: 85

ISBN-13: 0309045460

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Initial priorities for U.S. participation in the International Decade for Natural Disaster Reduction, declared by the United Nations, are contained in this volume. It focuses on seven issues: hazard and risk assessment; awareness and education; mitigation; preparedness for emergency response; recovery and reconstruction; prediction and warning; learning from disasters; and U.S. participation internationally. The committee presents its philosophy of calls for broad public and private participation to reduce the toll of disasters.

Precipitation: Advances in Measurement, Estimation and Prediction
Precipitation: Advances in Measurement, Estimation and Prediction

Author: Silas C. Michaelides

Publisher: Springer Science & Business Media

Published: 2008-02-27

Total Pages: 552

ISBN-13: 3540776559

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This volume is the outcome of contributions from 51 scientists who were invited to expose their latest findings on precipitation research and in particular, on the measurement, estimation and prediction of precipitation. The reader is presented with a blend of theoretical, mathematical and technical treatise of precipitation science but also with authentic applications, ranging from local field experiments and country-scale campaigns to multinational space endeavors.

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA
Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Author: Elias T. Krainski

Publisher: CRC Press

Published: 2018-12-07

Total Pages: 284

ISBN-13: 0429629850

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Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.

Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning
Rainfall Thresholds and Other Approaches for Landslide Prediction and Early Warning

Author: Samuele Segoni

Publisher: MDPI

Published: 2021-06-22

Total Pages: 222

ISBN-13: 3036509305

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Landslides are destructive processes causing casualties and damage worldwide. The majority of the landslides are triggered by intense and/or prolonged rainfall. Therefore, the prediction of the occurrence of rainfall-induced landslides is an important scientific and social issue. To mitigate the risk posed by rainfall-induced landslides, landslide early warning systems (LEWS) can be built and applied at different scales as effective non-structural mitigation measures. Usually, the core of a LEWS is constituted of a mathematical model that predicts landslide occurrence in the monitored areas. In recent decades, rainfall thresholds have become a widespread and well established technique for the prediction of rainfall-induced landslides, and for the setting up of prototype or operational LEWS. A rainfall threshold expresses, with a mathematic law, the rainfall amount that, when reached or exceeded, is likely to trigger one or more landslides. Rainfall thresholds can be defined with relatively few parameters and are very straightforward to operate, because their application within LEWS is usually based only on the comparison of monitored and/or forecasted rainfall. This Special Issue collects contributions on the recent research advances or well-documented applications of rainfall thresholds, as well as other innovative methods for landslide prediction and early warning. Contributions regarding the description of a LEWS or single components of LEWS (e.g., monitoring approaches, forecasting models, communication strategies, and emergency management) are also welcome. We encourage, in particular, the submission of contributions concerning the definition and validation of rainfall thresholds, and their operative implementation in LEWS. Other approaches for the forecasting of landslides are also of interest, such as physically based modelling, hazard mapping, and the monitoring of hydrologic and geotechnical indicators, especially when described in the framework of an operational or prototype early warning system.

Modern Technologies for Landslide Monitoring and Prediction
Modern Technologies for Landslide Monitoring and Prediction

Author: Marco Scaioni

Publisher: Springer

Published: 2015-01-23

Total Pages: 251

ISBN-13: 3662459310

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Modern Technologies for Landslide Investigation and Prediction presents eleven contributed chapters from Chinese and Italian authors, as a follow-up of a bilateral workshop held in Shanghai on September 2013. Chapters are organized in three main parts: ground-based monitoring techniques (photogrammetry, terrestrial laser scanning, ground-based InSAR, infrared thermography, and GNSS networks), geophysical (passive seismic sensor networks) and geotechnical methods (SPH and SLIDE), and satellite remote-sensing techniques (InSAR and optical images). Authors of these contributes are internationally-recognized experts in their respective research fields. Marco Scaioni works in the college of Surveying and Geo-Informatics at Tongji University, Shanghai (P.R. China). His research fields are mainly Close-range Photogrammetry, Terrestrial Laser Scanning, and other ground-based sensors for metrological and deformation monitoring applications to structural engineering and geosciences. In the period 2012-2016 he is chairman of the Working Group V/3 in the International Society for Photogrammetry and Remote Sensing, focusing on ‘Terrestrial 3D Imaging and Sensors’.

Geohazard Mitigation
Geohazard Mitigation

Author: Basanta Raj Adhikari

Publisher: Springer Nature

Published: 2021-11-26

Total Pages: 485

ISBN-13: 9811661405

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This book presents the select proceedings of the Virtual Conference on Disaster Risk Reduction (VCDRR 2021). It emphasizes on the role of civil engineering for a disaster resilient society. It presents latest research in geohazards and their mitigation. Various topics covered in this book are land use, ground response, liquefaction, and disaster mitigation techniques. This book is a comprehensive volume on disaster risk reduction (DRR) and its management for a sustainable built environment. This book will be useful for the students, researchers, policy makers and professionals working in the area of civil engineering, especially disaster management.

Utilizing AI and Machine Learning for Natural Disaster Management
Utilizing AI and Machine Learning for Natural Disaster Management

Author: Satishkumar, D.

Publisher: IGI Global

Published: 2024-04-29

Total Pages: 374

ISBN-13:

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Acute events of natural origin, spanning atmospheric, biological, geophysical, hydrologic, and oceanographic realms, persistently menace societies globally. Approximately 160 million people annually bear the brunt of these disasters, with certain regions facing disproportionate impacts. The lack of predictability intensifies the challenge, creating intercommunal capacity gaps and amplifying the dire consequences. Utilizing AI and Machine Learning for Natural Disaster Management provides instances of ML in predicting earthquakes. By leveraging seismic data, AI systems can analyze magnitude and patterns, providing invaluable insights to forecast earthquake occurrences and aftershocks. Similarly, the book unveils the potential of ML in simulating floods by recording and analyzing rainfall patterns from previous years. The predictive power extends to hurricanes, where data on wind speed, rainfall, temperature, and moisture converge to anticipate future occurrences, potentially saving millions in property damage.