Table 2 Previous review articles limitations.

From: Enhanced slope stability prediction using ensemble machine learning techniques

Feature

Proposed method

Zhang et al. (2020)

Li et al. (2021)

Doe et al. (2019)

Smith et al. (2022)

Brown et al. (2023)

Wang et al. (2018)

Chen et al. (2020)

Data preprocessing

Standardization

X

X

X

X

X

Normalization

X

X

X

X

Discretization

X

X

X

X

X

X

X

Key factors analyzed

Slope height

X

X

X

X

X

X

Joint friction angle

X

X

X

X

X

X

X

Joint cohesion

X

X

X

X

X

X

X

Bench width

X

X

X

X

X

X

X

Bench height

X

X

X

X

X

X

X

Slope angle

X

X

X

X

X

X

Machine learning models

K-NN

X

X

X

X

X

X

X

Decision tree (DT)

X

X

X

X

X

X

Random forest (RF)

X

X

X

Support vector machine (SVM)

X

X

X

X

Bagging

X

X

X

X

Boosting

X

X

X

X

X

Dimensionality reduction

Kernel PCA

X

X

X

X

X

X

PCA

X

X

X

X

LDA

X

X

X

X

X

X

t-SNE

X

X

X

X

X

X

Classification accuracy ensemble models like bagging and boosting

>90%

X

X

X

X

X

X

\(\sim\)85%

X

X

X

X

X

X

\(\sim\)83%

X

X

X

X

X

X

Regression performance

Bagging

X

X

X

X

X

X

Lasso-Lars CV

X

X

X

X

X

X

RMSE < 0.6

X

X

X

X

X

\(\hbox {R}^{2}\) > 0.85

X

X

X

X

X

X

Ensemble learning

Bagging

X

X

X

X

Boosting

X

X

X

X

X

Dimension reduction impact

Necessary

X

X

X

X

X

X

Unnecessary

X

X

X

X

X

X

Key insights

Slope height significant

X

X

X

X

X

X

Soil cohesion

X

X

X

X

X

X

Slope gradient

X

X

X

X

X

X

Novelty

Ensemble & regression integration

X

X

X

X

X

X

RF adaptability

X

X

X

X

X

X

ANN for geotechnical prediction

X

X

X

X

X

X

XGBoost innovation

X

X

X

X

X

X