Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance

A Merghadi, AP Yunus, J Dou, J Whiteley… - Earth-Science …, 2020 - Elsevier
Landslides are one of the catastrophic natural hazards that occur in mountainous areas,
leading to loss of life, damage to properties, and economic disruption. Landslide …

Landslide susceptibility mapping using machine learning algorithm validated by persistent scatterer In-SAR technique

MA Hussain, Z Chen, Y Zheng, M Shoaib, SU Shah… - Sensors, 2022 - mdpi.com
Landslides are the most catastrophic geological hazard in hilly areas. The present work
intends to identify landslide susceptibility along Karakorum Highway (KKH) in Northern …

Landslide susceptibility mapping using machine learning algorithm: a case study along Karakoram Highway (KKH), Pakistan

MA Hussain, Z Chen, I Kalsoom, A Asghar… - Journal of the Indian …, 2022 - Springer
Abstract The China–Pakistan Karakoram Highway links China to South Asia and the Middle
East through Pakistan. Rockfall and debris flows are dangerous geological risks on the main …

[PDF][PDF] Landslide susceptibility mapping using machine learning algorithm

MA Hussain, Z Chen, R Wang, SU Shah, M Shoaib… - Civ. Eng. J, 2022 - researchgate.net
Landslides are natural disasters that have resulted in the loss of economies and lives over
the years. The landslides caused by the 2005 Muzaffarabad earthquake heavily impacted …

Application of machine learning methods for snow avalanche susceptibility mapping in the Parlung Tsangpo catchment, southeastern Qinghai-Tibet Plateau

H Wen, X Wu, X Liao, D Wang, K Huang… - Cold Regions Science …, 2022 - Elsevier
Determining the snow avalanche-prone areas is fundamental for risk mitigation in the snowy
mountains of the Qinghai-Tibet Plateau, especially in the context of current climate warming …

Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling

MT Riaz, M Basharat, QB Pham, Y Sarfraz… - Geocarto …, 2022 - Taylor & Francis
Landslide predictive performance is expected to vary with different sampling techniques,
such as landslide random and cluster sampling. Current advancements in remote sensing …

Predicting buoyant jet characteristics: a machine learning approach

H Hassanzadeh, S Joshi, SM Taghavi - Chemical Product and …, 2024 - degruyter.com
We study positively buoyant miscible jets through high-speed imaging and planar laser-
induced fluorescence methods, and we rely on supervised machine learning techniques to …

Estimating landslide hazard distribution based on machine learning and bivariate statistics in Utmah Region, Yemen

YM Khalil, YA Al-Masnay, NM Al-Areeq, AR Al-Aizari… - Natural Hazards, 2024 - Springer
Landslides represent significant risks to human activity, leading to infrastructure damage
and loss of life. This study focuses on assessing landslide hazards in Utmah Region …

Indexing complex networks for fast attributed kNN queries

S Kobayashi, S Matsugu, H Shiokawa - Social Network Analysis and …, 2022 - Springer
The k nearest neighbor (k NN) query is an essential graph data-management tool used for
finding relevant data entities suited to a user-specified query node. Graph indexing methods …

Exploring different approaches for landslide susceptibility zonation mapping in Manipur: a comparative study of AHP, FR, machine learning, and deep learning models

A Kshetrimayum, A Goyal - Journal of Spatial Science, 2024 - Taylor & Francis
The movement of rock, soil, and other debris down a slope or incline is a geological
phenomenon known as a landslide. To analyze the landslide susceptibility (LS) in Manipur …