Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
Landslides are one of the catastrophic natural hazards that occur in mountainous areas,
leading to loss of life, damage to properties, and economic disruption. Landslide …
leading to loss of life, damage to properties, and economic disruption. Landslide …
Salt stress in plants and mitigation approaches
Salinization of soils and freshwater resources by natural processes and/or human activities
has become an increasing issue that affects environmental services and socioeconomic …
has become an increasing issue that affects environmental services and socioeconomic …
Influence of data splitting on performance of machine learning models in prediction of shear strength of soil
QH Nguyen, HB Ly, LS Ho, N Al-Ansari… - Mathematical …, 2021 - Wiley Online Library
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate
Predicting the penetration rate is a complex and challenging task due to the interaction
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
A comparative study of kernel logistic regression, radial basis function classifier, multinomial naïve bayes, and logistic model tree for flash flood susceptibility mapping
Risk of flash floods is currently an important problem in many parts of Vietnam. In this study,
we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial …
we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial …
Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars
Despite the extensive use of mortar materials in constructions over the last decades, there is
not yet a robust quantitative method, available in the literature, which can reliably predict …
not yet a robust quantitative method, available in the literature, which can reliably predict …
A novel hybrid soft computing model using random forest and particle swarm optimization for estimation of undrained shear strength of soil
Determination of shear strength of soil is very important in civil engineering for foundation
design, earth and rock fill dam design, highway and airfield design, stability of slopes and …
design, earth and rock fill dam design, highway and airfield design, stability of slopes and …
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility
Land subsidence (LS) is a significant problem that can cause loss of life, damage property,
and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a …
and disrupt local economies. The Semnan Plain is an important part of Iran, where LS is a …
Examining hybrid and single SVM models with different kernels to predict rock brittleness
The aim of this study was twofold:(1) to assess the performance accuracy of support vector
machine (SVM) models with different kernels to predict rock brittleness and (2) compare the …
machine (SVM) models with different kernels to predict rock brittleness and (2) compare the …
GIS-based gully erosion susceptibility mapping: a comparison of computational ensemble data mining models
Gully erosion destroys agricultural and domestic grazing land in many countries, especially
those with arid and semi-arid climates and easily eroded rocks and soils. It also generates …
those with arid and semi-arid climates and easily eroded rocks and soils. It also generates …