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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In this study, we have investigated rainfall induced landslide susceptibility of the Uttarkashi district of India through the developmentof different novel GIS based soft computing approaches namely Bagging-MLPC, Dagging-MLPC, Decorate-MLPC which are a combination Multi-layer Perceptron Neural Network Classifier  ( MLPC) and Bagging, Dagging, and Decorate ensemble methods, respectively. The proposed models were trained and validated with the help of 103 historical landslide events (divided into 2 samples: training (70%) and validation (30%)) and 12 landslide conditioning factors. The accuracy of the models was evaluated using different statistical methods including Area Under Curve (AUC) of Receiver Operating Characteristic (ROC). The results show that though performance of all the studied models is good (AUC &gt; 0.80) but of the hybrid Bagging-MLPC model is the best (AUC:0.965). Therefore, this newly hybrid model (Bagging-MLPC) can be used for the accurate landslide susceptibility mapping and assessment of landslide prone areas for landslide prevention and management.

Rainfall induced landslide susceptibility mapping using novel hybrid soft computing methods based on multi-layer perceptron neural network classifier