[HTML][HTML] Deep learning for geological hazards analysis: Data, models, applications, and opportunities
As natural disasters are induced by geodynamic activities or abnormal changes in the
environment, geological hazards tend to wreak havoc on the environment and human …
environment, geological hazards tend to wreak havoc on the environment and human …
A comprehensive review of machine learning‐based methods in landslide susceptibility map**
S Liu, L Wang, W Zhang, Y He, S Pijush - Geological Journal, 2023 - Wiley Online Library
Landslide susceptibility map** (LSM) has been widely used as an important reference for
development and construction planning to mitigate the potential social‐eco impact caused …
development and construction planning to mitigate the potential social‐eco impact caused …
[HTML][HTML] Landslide susceptibility map** using hybrid random forest with GeoDetector and RFE for factor optimization
The present study aims to develop two hybrid models to optimize the factors and enhance
the predictive ability of the landslide susceptibility models. For this, a landslide inventory …
the predictive ability of the landslide susceptibility models. For this, a landslide inventory …
[HTML][HTML] Soil water erosion susceptibility assessment using deep learning algorithms
Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land
degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem …
degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem …
[HTML][HTML] Landslide susceptibility map** using machine learning: A literature survey
Landslide is a devastating natural disaster, causing loss of life and property. It is likely to
occur more frequently due to increasing urbanization, deforestation, and climate change …
occur more frequently due to increasing urbanization, deforestation, and climate change …
Landslide detection using deep learning and object-based image analysis
Recent landslide detection studies have focused on pixel-based deep learning (DL)
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …
Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility map** in Icheon, South Korea
Landslides are a geological hazard that can pose a serious threat to human health and the
environment of highlands or mountain slopes. Landslide susceptibility map** is an …
environment of highlands or mountain slopes. Landslide susceptibility map** is an …
[HTML][HTML] Ensemble learning framework for landslide susceptibility map**: Different basic classifier and ensemble strategy
The application of ensemble learning models has been continuously improved in recent
landslide susceptibility research, but most studies have no unified ensemble framework …
landslide susceptibility research, but most studies have no unified ensemble framework …
Riverside landslide susceptibility overview: leveraging artificial neural networks and machine learning in accordance with the United Nations (UN) sustainable …
Riverside landslides present a significant geohazard globally, posing threats to
infrastructure and human lives. In line with the United Nations' Sustainable Development …
infrastructure and human lives. In line with the United Nations' Sustainable Development …
Identifying the essential conditioning factors of landslide susceptibility models under different grid resolutions using hybrid machine learning: A case of Wushan and …
This study attempts to identify the essential conditioning factors of landslides to increase the
predictive ability of landslide susceptibility models and explore the effects of different grid …
predictive ability of landslide susceptibility models and explore the effects of different grid …