English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Over the years, landslide has become one of the most destructive events that can happen in hilly areas. Tehri, a region in the Himalayas is no different. Current research aids in the construction of ensemble models of DLNN and SVM, which are then compared with various SVM kernels. Landslide susceptibility mapping in the Tehri region of the Himalayas has been worked upon using a deep learning (DLNN), four machine learnings (SVM-RBF, SVM-Linear, SVM-Polynomial, SVM-Sigmoid), and their novel ensembles i.e., DLNN with SVM-RBF, DLNN with SVM-Linear, DLNN with SVM-Polynomial and DLNN with SVM-Sigmoid. 16 geo-environmental landslide conditioning factors (LCFs) have been considered. These models were trained using 70% of inventory landslides and tested using 30% of the same. The results revealed the superiority of DLNN, DLNN-SVM (RBF), DLNN-SVM (Linear) models which quantified 28.32, 26.96 and 22.41% of the area highly susceptible for landslide, respectively.

Novel ensemble of deep learning neural network and support vector machine for landslide susceptibility mapping in Tehri region, Garhwal Himalaya