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

Abstract

Background: 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. Results: 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. Conclusions: 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.

Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

“…A certain number of sample data pieces of nonlandslide points need to be selected from the study area using random sampling methods to construct a binary classification model. Studies have shown that, in susceptibility assessment, the nonevent (nonlandslide points) sample size can be 2-10 times greater than the events (landslide points) (King and Zeng, 2001;Nam and Wang, 2019). After six experiments (the ratios of landslide and nonlandslide points were 1:5; 1:6; 1:7; 1:8; 1:9, and 1:10, respectively), the final ratio of landslide to nonlandslide was determined as 1:10; that is, 2,340 nonlandslide samples were selected.…”

Machine learning for landslides prevention: a survey

“…The autoencoder is trained to reconstruct the input of the landslide-influencing factors onto the output layer for feature extraction and dimensionality reduction. The methods prevent the simple copying of the data and the network [121]. Maher et al [146] used an autoencoder as an optimized factor to learn features from a dataset in an unsupervised manner [58].…”

Machine Learning for Landslides Prevention: A Survey

“…The autoencoder is trained to reconstruct the input of the landslide-influencing factors onto the output layer for feature extraction and dimensionality reduction. The methods prevent the simple copying of the data and the network [135].…”