A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps-Reference-Cited by-同舟云学术

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A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps

Published:2023-02 Issue: Volume:858 Page:159412
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Bozzolan Elisa,Holcombe Elizabeth A.,Pianosi Francesca,Marchesini Ivan,Alvioli Massimiliano,Wagener Thorsten

Funder

Alexander von Humboldt-Stiftung

EPSRC

Bundesministerium für Bildung und Forschung

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference76 articles.

1. Dealing with deep uncertainties in landslide modelling for disaster risk reduction under climate change;Almeida;Nat. Hazards Earth Syst. Sci.,2017

2. Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling;Alvioli;Geosci. Model Dev.,2016

3. Implications of climate change on landslide hazard in Central Italy;Alvioli;Sci. Total Environ.,2018

4. Parameter-free delineation of slope units and terrain subdivision of Italy;Alvioli;Geomorphology,2020

5. Geomorphological slope units of the Himalayas;Alvioli;J.Maps,2022

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1. Freeze-thaw landslide susceptibility assessment and its future development on the seasonally frozen ground of the Qinghai-Tibet Plateau under warming-humidifying climate;Cold Regions Science and Technology;2024-11

2. Synthetic libraries of urban landslide simulations to identify slope failure hotspots and drivers across spatial scales and landscapes;Landslides;2024-09-13

3. Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions;Computers and Geotechnics;2024-08

4. Landslide Susceptibility Assessment Considering Time-Varying of Dynamic Factors;Natural Hazards Review;2024-08

5. Optimizing the Application of Machine Learning Models in Predicting Landslide Susceptibility Using the Information Value Model in Junlian County of Sichuan Basin;2024-07-10

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