  Landslides are a common natural disaster that can cause casualties, property
safety threats and economic losses. Therefore, it is important to understand or
predict the probability of landslide occurrence at potentially risky sites. A
commonly used means is to carry out a landslide susceptibility assessment based
on a landslide inventory and a set of landslide contributing factors. This can
be readily achieved using machine learning (ML) models such as logistic
regression (LR), support vector machine (SVM), random forest (RF), extreme
gradient boosting (Xgboost), or deep learning (DL) models such as convolutional
neural network (CNN) and long short time memory (LSTM). As the input data for
these models, landslide contributing factors have varying influences on
landslide occurrence. Therefore, it is logically feasible to select more
important contributing factors and eliminate less relevant ones, with the aim
of increasing the prediction accuracy of these models. However, selecting more
important factors is still a challenging task and there is no generally
accepted method. Furthermore, the effects of factor selection using various
methods on the prediction accuracy of ML and DL models are unclear. In this
study, the impact of the selection of contributing factors on the accuracy of
landslide susceptibility predictions using ML and DL models was investigated.
Four methods for selecting contributing factors were considered for all the
aforementioned ML and DL models, which included Information Gain Ratio (IGR),
Recursive Feature Elimination (RFE), Particle Swarm Optimization (PSO), Least
Absolute Shrinkage and Selection Operators (LASSO) and Harris Hawk Optimization
(HHO). In addition, autoencoder-based factor selection methods for DL models
were also investigated. To assess their performances, an exhaustive approach
was adopted,...
