https://www.selleckchem.com/products/nvs-stg2.html
904 - 0.919). Whereas, testing with LR (AUC 0.825 - 0.785) and NNET (AUC 0.882 - 0.858) produces larger differences in the accuracies between the four datasets. Nonetheless, the highest success rates were obtained for samples within the landslide scarp area. The analogy was then validated with a published landslide inventory from the 2015 Gorkha earthquake. We, therefore, suggest that DNN models as an appropriate technique to increase the predictive performance of landslide susceptibilities if the landslide scarp and body are not chara
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