[HTML][HTML] Landslide failures detection and mapping using Synthetic Aperture Radar: Past, present and future

AC Mondini, F Guzzetti, KT Chang, O Monserrat… - Earth-Science …, 2021 - Elsevier
Landslides are geomorphological processes that shape the landscapes of all continents,
dismantling mountains and contributing sediments to the river networks. Caused by …

[HTML][HTML] Landslides in a changing climate

SL Gariano, F Guzzetti - Earth-science reviews, 2016 - Elsevier
Warming of the Earth climate system is unequivocal. That climate changes affect the stability
of natural and engineered slopes and have consequences on landslides, is also …

Ensemble boosting and bagging based machine learning models for groundwater potential prediction

A Mosavi, F Sajedi Hosseini, B Choubin… - Water Resources …, 2021 - Springer
Due to the rapidly increasing demand for groundwater, as one of the principal freshwater
resources, there is an urge to advance novel prediction systems to more accurately estimate …

[HTML][HTML] Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia

AM Youssef, HR Pourghasemi - Geoscience Frontiers, 2021 - Elsevier
The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …

[HTML][HTML] An explainable AI (XAI) model for landslide susceptibility modeling

B Pradhan, A Dikshit, S Lee, H Kim - Applied Soft Computing, 2023 - Elsevier
Landslides are among the most devastating natural hazards, severely impacting human
lives and damaging property and infrastructure. Landslide susceptibility maps, which help to …

Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

JN Goetz, A Brenning, H Petschko, P Leopold - Computers & geosciences, 2015 - Elsevier
Statistical and now machine learning prediction methods have been gaining popularity in
the field of landslide susceptibility modeling. Particularly, these data driven approaches …

Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran

O Rahmati, HR Pourghasemi, H Zeinivand - Geocarto International, 2016 - Taylor & Francis
Flood is one of the most devastating natural disasters with socio-economic and
environmental consequences. Thus, comprehensive flood management is essential to …

[PDF][PDF] Changes in climate extremes and their impacts on the natural physical environment

N Nicholls, D Easterling, CM Goodess… - Managing the risks …, 2012 - library.harvard.edu
A changing climate can lead to changes in the frequency, intensity or duration of an extreme
event, or result in an 3 unprecedented, previously unobserved, extreme. As well, a weather …

Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate …

H Hong, HR Pourghasemi, ZS Pourtaghi - Geomorphology, 2016 - Elsevier
Landslides are an important natural hazard that causes a great amount of damage around
the world every year, especially during the rainy season. The Lianhua area is located in the …

Landslide susceptibility mapping using different GIS-based bivariate models

E Nohani, M Moharrami, S Sharafi, K Khosravi… - Water, 2019 - mdpi.com
Landslides are the most frequent phenomenon in the northern part of Iran, which cause
considerable financial and life damages every year. One of the most widely used …