Reprint

Landslide Hazard and Environment Risk Assessment

Edited by
April 2022
318 pages
  • ISBN978-3-0365-3693-4 (Hardback)
  • ISBN978-3-0365-3694-1 (PDF)

This book is a reprint of the Special Issue Landslide Hazard and Environment Risk Assessment that was published in

Business & Economics
Environmental & Earth Sciences
Summary

Landslides are among the most widespread and frequent natural hazards. Landsliding is linked to the combination of geological, geomorphological, and climatic factors in response to trigger mechanisms, mostly represented by heavy rainfall events, seismicity, or human action. Landslides directly and indirectly impact a territory, causing fatalities and huge socio-economic losses. Consequently, to avoid serious consequences and support sustainable territorial planning, there is a clear need of correct land use policies and best practices for long-term risk mitigation and reduction. In this context, geomorphological field activities, satellite remote sensing, landslide susceptibility mapping, and innovative GIS analysis offer effective support for mapping and monitoring landslides’ activity at both the local and regional scales. All landslide types are considered, from rockfalls to debris flows, from slow-moving slides to very rapid rock avalanches. Contributions to this Special Issue report key advances in landslide susceptibility mapping, environmental risk management in mass movement-prone areas, and landslide analysis in different geomorphological/morphostructural environments. Each article describes a distinct methodological approach to accurately investigate landslide phenomena and assess slope stability. Each article provides a scientific basis useful for the implementation of land planning, civil protection activities, and mitigation measures in different geological–geomorphological frameworks.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
Kenya; landslide susceptibility; fuzzy analytic hierarchy process; triangular fuzzy numbers; GIS; interaction matrix; heuristic; susceptibility; inventory; Greece; historical landslides; multitemporal analysis; geomorphological mapping; GIS analysis; piedmont area; Abruzzo Region; landslide; hydromechanical modeling; early-warning; slope stability; rainfall-induced landslides; local factor of safety; SoilNet; geophysical characterization; water content distribution; bedrock topography; large-scale landslides; DSGSDs; normal faults and overthrusts; Sibillini Mts.; Central Apennines; landslide; Italy; risk; soil sealing; landslides; factor of safety; numerical models; Hoek–Brown method; monoclinal setting; landslides; susceptibility; hybrid modeling; Geographical Detector; information value; Greece; landslide; susceptibility; machine learning; GIS; Kerala; hazard; landslide; hydroseeding; slope; vegetation; AHP; snow avalanche; mass movements-prone areas; hazard assessment; climate extremization; environmental risk; Gran Sasso Massif; Central Apennines; morphotectonic; morphostratigraphy; DGSDs; river capture; fluvial terraces; Sardinia; Italy; n/a