There has been an increasing demand for underground construction due to urbanization
and limited land in metropolitan cities in the recent years. However, the behavior of …

We present a hybrid intelligent approach based on Naïve Bayes trees (NBT) and random
subspace (RS) ensemble for landslide susceptibility mapping at the Bijar region, Kurdistan …

This study evaluated and compared landslide susceptibility maps produced with three
different methods, frequency ratio, weights of evidence, and logistic regression, by using …

The main aim of this study was to compare the performances of the hybrid approaches of
traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE …

In this study, groundwater spring potential maps produced by three different methods,
frequency ratio, weights of evidence, and logistic regression, were evaluated using …

This study proposes a comprehensive methodology to the application of an Artificial Neural
Network Ensemble (ANNE) for the mapping of landslide susceptibility. The identification of …

The main aim of this study is to use weights of evidence (WoE), logistic regression (LR),
naïve Bayes (NB), and alternating decision tree (ADTree) models to draw a landslide …

Expected increases in intensity and frequency of rainfall extremes due to climate change,
and increased paving and loss of water storage space in urban areas is making cities more …

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA)
which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive …

This study compares the predictive performance of GIS-based landslide susceptibility
mapping (LSM) using four different kernel functions in support vector machines (SVMs) …