Omid Ghorbanzadeh

TL;DR: The CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner, Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the results of augmentation strategies to artificially increase the number of existing samples are better understanding.

TL;DR: Two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional Neural Network (CNN), are applied for national-scale landslide susceptibility mapping of Iran to generate landslide susceptibility maps of Iran.

TL;DR: In this paper, the authors compared the performance of two multi-criteria decision analysis (MCDA) models including analytical hierarchical process (AHP) and analytical network process (ANP) and two machine learning models including random forest (RF) and support vector machine (SVM).

TL;DR: A methodology capable of dealing with the inconsistencies and uncertainties of users’ responses by applying an Interval Analytic Hierarchy Process (IAHP) through comparing the results of passengers to reference stakeholder groups is developed.

TL;DR: A methodology that incorporates object-based image analysis with three machine learning methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal.