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

Thousands of landslides were triggered by the Hokkaido Eastern Iburi earthquake on 6 September 2018 in Iburi regions of Hokkaido, Northern Japan. Most of the landslides (5627 points) occurred intensively between the epicenter and the station that recorded the highest peak ground acceleration. Hundreds of aftershocks followed the major shocks. Moreover, in Iburi region, there is a high possibility of earthquakes occurring in the future. Effective prediction and susceptibility assessment methods are required for sustainable management and disaster mitigation in the study area. The aim of this study is to evaluate the performance of an autoencoder framework based on deep neural network for prediction and susceptibility assessment of regional landslides triggered by earthquakes. By applying 12 sampling sizes and 12 landslide-influencing factors, 12 landslide susceptibility maps were produced using an autoencoder framework. The results of the model were evaluated using qualitative and quantitative assessment methods. The ratios of the sampling sizes on the non-landslide points randomly generated from the combination zone including plain and mountain (PM) and a mountainous only zone (M) affected different prediction abilities of the model’s performance. The 12 susceptibility maps, including the landslide susceptibility index, indicated the various spatial distributions of the landslide susceptibility values in both PM and the M. The highly accurate models explicitly distinguished the potential areas of landslide from stable areas without expanding the spatial extent of the potential landslide areas. The autoencoder is proved to be an effective and efficient method for extracting spatial patterns through unsupervised learning for the prediction and susceptibility assessment of landslide areas.

geoenvironmental disasters

The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan