[HTML][HTML] Landslide susceptibility mapping with deep learning algorithms
Among natural hazards, landslides are devastating in China. However, little is known
regarding potential landslide-prone areas in Maoxian County. The goal of this study was to …
regarding potential landslide-prone areas in Maoxian County. The goal of this study was to …
GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach
The May 2nd and 3rd, 2023 landslide in Rwanda's Western Province caused a devastating
natural disaster, resulting in the tragic loss of 95 lives. Ngororero, Rubavu, Nyabihu, and …
natural disaster, resulting in the tragic loss of 95 lives. Ngororero, Rubavu, Nyabihu, and …
Hazard assessment for regional typhoon-triggered landslides by using physically-based model–a case study from southeastern China
Landslide hazard assessment is an important component of risk management and land-use
planning. This study aims to investigate the application of a physically-based model named …
planning. This study aims to investigate the application of a physically-based model named …
Landslide susceptibility and influencing factors analysis in Rwanda
Rwanda as a landlocked country has been recurrently facing tremendous and devastating
landslides having serious impacts on the environment and socioeconomic development …
landslides having serious impacts on the environment and socioeconomic development …
Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China
Y Huan, L Song, U Khan, B Zhang - Environmental Earth Sciences, 2023 - Springer
The current study aims to apply and compare the performance of six machine learning
algorithms, including three basic classifiers: random forest (RF), gradient boosting decision …
algorithms, including three basic classifiers: random forest (RF), gradient boosting decision …
Assessing wetland habitat vulnerability in moribund Ganges delta using bivariate models and machine learning algorithms
S Pal, S Paul - Ecological Indicators, 2020 - Elsevier
The present study aims to measure wetland habitat vulnerability (WHV) in moribund deltaic
part of India using ten conditioning parameters eg, WPF, water depth, change in WPF …
part of India using ten conditioning parameters eg, WPF, water depth, change in WPF …
Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis
CAS Camera, G Bajni, I Corno, M Raffa… - Science of the total …, 2021 - Elsevier
The study objective was to derive a susceptibility model for shallow landslides that could
include process-related non-stationary variables, to be adaptable to climate changes. We …
include process-related non-stationary variables, to be adaptable to climate changes. We …
Exploring a form of pixel-based information value model for flood probability assessment and geo-visualization over an East African basin: a case of Nyabarongo in …
Abstract The Nyabarongo basin in Rwanda is subjected to hydrometeorological hazards,
particularly floods, which are the most prevailing and devastating. Therefore, understanding …
particularly floods, which are the most prevailing and devastating. Therefore, understanding …
Modelling water richness in riparian flood plain wetland using bivariate statistics and machine learning algorithms and figuring out the role of damming
S Pal, R Sarda - Geocarto International, 2022 - Taylor & Francis
The present work tried to develop multiparametric statistical and machine learning models of
wetland water richness at two phases in the pre-dam period and at one phase in the post …
wetland water richness at two phases in the pre-dam period and at one phase in the post …
Machine Learning–Based Systems for Early Warning of Rainfall-Induced Landslide
Z Zheng, K Zhang, N Wang, M Zhu, Z He - Natural Hazards Review, 2024 - ascelibrary.org
Landslide disasters have inflicted incalculable losses on China's national economy, as well
as on lives and property. Notably, 90% of landslide disasters are directly induced by rainfall …
as on lives and property. Notably, 90% of landslide disasters are directly induced by rainfall …