Application of deep learning algorithms in geotechnical engineering: a short critical review
W Zhang, H Li, Y Li, H Liu, Y Chen, X Ding - Artificial Intelligence Review, 2021 - Springer
With the advent of big data era, deep learning (DL) has become an essential research
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …
subject in the field of artificial intelligence (AI). DL algorithms are characterized with powerful …
[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 …
[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 …
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 …
Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning
Automatic inspection for crack detection and estimation of the crack depth is critical in
assessing the damage and determining the appropriate method of repair in concrete …
assessing the damage and determining the appropriate method of repair in concrete …
Review on remote sensing methods for landslide detection using machine and deep learning
Landslide, one of the most critical natural hazards, is caused due to specific compositional
slope movement. In the past decades, due to inflation of urbanized area and climate change …
slope movement. In the past decades, due to inflation of urbanized area and climate change …
Landslide susceptibility map** using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region
Landslides are one of the most widespread natural disasters and pose severe threats to
people, properties, and the environment in many areas. Landslide susceptibility map** …
people, properties, and the environment in many areas. Landslide susceptibility map** …
Landslide hazard assessment based on Bayesian optimization–support vector machine in Nan** City, China
Landslide hazard assessment is critical for preventing and mitigating landslide disasters.
The tuning of hyperparameters is of great importance to achieve better accuracy in a …
The tuning of hyperparameters is of great importance to achieve better accuracy in a …
Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples
Abstract Machine learning models have been widely used for landslide susceptibility
assessment (LSA) in recent years. The accuracy of machine learning-based LSA often …
assessment (LSA) in recent years. The accuracy of machine learning-based LSA often …
Uncertainty analysis of non-landslide sample selection in landslide susceptibility prediction using slope unit-based machine learning models
The selection of non-landslide samples has a great impact on the machine learning
modelling for landslide susceptibility prediction (LSP). This study presents a novel …
modelling for landslide susceptibility prediction (LSP). This study presents a novel …