Inge Revhaug

TL;DR: In this paper, the authors evaluated and compared the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam.

TL;DR: In this article, two back-propagation training algorithms, Levenberg-Marquardt and Bayesian regularization, were utilized to determine synoptic weights using a training dataset.

TL;DR: In this paper, the authors proposed and verified a novel ensemble methodology that could improve prediction performance of landslide susceptibility models based on the functional tree classifier and three current state-of-the art machine learning ensemble frameworks, Bagging, AdaBoost, and MultiBoost.

TL;DR: RS–NBT is promising which can be utilized for landslide susceptibility assessment in other landslide-prone areas and improved significantly the performance of the NBT base classifier.

TL;DR: The findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform in an efficient manner.