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 …

Landslide susceptibility corresponds to the probability of landslide occurrence across a
given geographic space. This probability is usually estimated by using a binary classifier …

Landslides are considered as one of the most devastating natural hazards in Iran, causing
extensive damage and loss of life. Landslide susceptibility maps for landslide prone areas …

The present study aims to develop two hybrid models to optimize the factors and enhance
the predictive ability of the landslide susceptibility models. For this, a landslide inventory …

不同机器学习预测滑坡易发性的建模过程及其不确定性有所差异, 另外如何有效识别滑坡易发性
的主控因子意义重大. 针对上述问题, 以支持向量机(support vector machine, 简称SVM) …

Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These
landslides can be deadly to the community living downslope with their fast pace, turning …

Abstract Machine learning algorithms are an important measure with which to perform
landslide susceptibility assessments, but most studies use GIS-based classification methods …

Hazards and disasters have always negative impacts on the way of life. Landslide is an
overwhelming natural as well as man-made disaster that causes loss of natural resources …

The main aim of the present study is to explore and compare three state-of-the art data
mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide …

To develop a better spatial prediction model of landslide susceptibility along mountain
highways, this study compared assessment models of landslide susceptibility along …