Application of artificial intelligence in geotechnical engineering: A state-of-the-art review

A Baghbani, T Choudhury, S Costa, J Reiner - Earth-Science Reviews, 2022 - Elsevier
Geotechnical engineering deals with soils and rocks and their use in engineering
constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of …

Machine learning and landslide studies: recent advances and applications

FS Tehrani, M Calvello, Z Liu, L Zhang, S Lacasse - Natural Hazards, 2022 - Springer
Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …

Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon …

A Aditian, T Kubota, Y Shinohara - Geomorphology, 2018 - Elsevier
This study aims to evaluate landslide causative factors in landslide susceptibility
assessments and to compare landslide susceptibility models based on the bivariate …

Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks

S Ji, D Yu, C Shen, W Li, Q Xu - Landslides, 2020 - Springer
Convolution neural network (CNN) is an effective and popular deep learning method which
automatically learns complicated non-linear mapping from original inputs to given labels or …

Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression …

B Kalantar, B Pradhan, SA Naghibi… - … , Natural Hazards and …, 2018 - Taylor & Francis
Landslide is a natural hazard that results in many economic damages and human losses
every year. Numerous researchers have studied landslide susceptibility mapping (LSM) …

Riverside landslide susceptibility overview: leveraging artificial neural networks and machine learning in accordance with the United Nations (UN) sustainable …

YA Nanehkaran, B Chen, A Cemiloglu, J Chen… - Water, 2023 - mdpi.com
Riverside landslides present a significant geohazard globally, posing threats to
infrastructure and human lives. In line with the United Nations' Sustainable Development …

Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region

Y Yi, Z Zhang, W Zhang, H Jia, J Zhang - Catena, 2020 - Elsevier
Landslides are one of the most widespread natural disasters and pose severe threats to
people, properties, and the environment in many areas. Landslide susceptibility mapping …

Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size

P Tsangaratos, I Ilia - Catena, 2016 - Elsevier
The main objective of the present study was to compare the performance of a classifier that
implements the Logistic Regression and a classifier that employs a Naïve Bayes algorithm in …

Recommendations for the quantitative analysis of landslide risk

J Corominas, C van Westen, P Frattini… - Bulletin of engineering …, 2014 - Springer
This paper presents recommended methodologies for the quantitative analysis of landslide
hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and …

[HTML][HTML] Landslide susceptibility mapping and dynamic response along the Sichuan-Tibet transportation corridor using deep learning algorithms

W Huang, M Ding, Z Li, J Yu, D Ge, Q Liu, J Yang - Catena, 2023 - Elsevier
Landslides are one of the most serious natural hazards along the Sichuan-Tibet
transportation corridor, which crosses the most complicated region in the world in terms of …