M. B. Dholakia

TL;DR: Analysis of results indicates that landslide models using machine learning ensemble frameworks are promising methods which can be used as alternatives of individual base classifiers for landslide susceptibility assessment of other prone areas.

TL;DR: Analysis and comparison of the results show that all five landslide models performed well for landslide susceptibility assessment, but it has been observed that the SVM model has the best performance in comparison to other landslide models.

TL;DR: The objective of this study is to make a comparison of the prediction performance of three techniques, Functional Trees (FT), Multilayer Perceptron Neural Networks (MLP Neural Nets), and Naïve Bayes (NB) for landslide susceptibility assessment at the Uttarakhand Area (India).

TL;DR: The results show that the proposed RFRBF model has the highest prediction capability in comparison to the other models (LR, MLP Neural Nets, NB, and RFDT); therefore, the model is promising and should be used as an alternative technique for landslide susceptibility modeling.

TL;DR: In this article, the least square support vector machine (LSSVM) and multiclass alternating decision tree (MADT) techniques were compared for the spatial prediction of landslides.