Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility
In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on
statistical or machine learning approaches, have become popular to estimate the relative …
statistical or machine learning approaches, have become popular to estimate the relative …
Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
The quality of digital elevation models (DEMs), as well as their spatial resolution, are
important issues in geomorphic studies. However, their influence on landslide susceptibility …
important issues in geomorphic studies. However, their influence on landslide susceptibility …
Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
Q He, H Shahabi, A Shirzadi, S Li, W Chen… - Science of the total …, 2019 - Elsevier
Landslides are major hazards for human activities often causing great damage to human
lives and infrastructure. Therefore, the main aim of the present study is to evaluate and …
lives and infrastructure. Therefore, the main aim of the present study is to evaluate and …
[HTML][HTML] The contribution of the frequency ratio model and the prediction rate for the analysis of landslide risk in the Tizi N'tichka area on the national road (RN9) …
Road infrastructure is vital for economic development, connecting various locations.
However, in Morocco, landslides pose recurring challenges to road projects due to factors …
However, in Morocco, landslides pose recurring challenges to road projects due to factors …
Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas
Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility mapping is a collection of landslide causative factors …
necessary for landslide susceptibility mapping is a collection of landslide causative factors …
[HTML][HTML] A unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood for landslide susceptibility mapping
Landslide susceptibility mapping (LSM) is very important for hazard risk identification and
prevention. Most of existing neural network models extract a pixel neighborhood feature or a …
prevention. Most of existing neural network models extract a pixel neighborhood feature or a …
Evaluation of different DEMs for gully erosion susceptibility mapping using in-situ field measurement and validation
The spatial variability in any kind of geomorphic studies based on terrain attributes are the
most important issues. This terrain attributes and their respective characteristics play a …
most important issues. This terrain attributes and their respective characteristics play a …
Stability prediction of a natural and man-made slope using various machine learning algorithms
In this paper, an attempt has been made to implement various machine learning techniques
to predict the factor of safety of a natural residual soil slope and a man-made overburden …
to predict the factor of safety of a natural residual soil slope and a man-made overburden …
GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques
X Zhao, W Chen - Applied Sciences, 2019 - mdpi.com
The main purpose of this paper is to use ensembles techniques of functional tree-based
bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT) …
bagging, rotation forest, and dagging (functional trees (FT), bagging-functional trees (BFT) …
Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms
W Chen, HR Pourghasemi, SA Naghibi - Bulletin of Engineering Geology …, 2018 - Springer
The main objective of the current study is to apply a random forest (RF) data-driven model
and prioritization of landslide conditioning factors according to this method and its …
and prioritization of landslide conditioning factors according to this method and its …