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.

中文翻译：

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使用自动编码器进行滑坡预测和敏感性评估的性能：以日本2018年北海道东部伊布里（Eburi）地震引发的滑坡为例

2018年9月6日，日本北部北海道依武里地区的北海道东部伊武里地震引发了数千起滑坡。大多数滑坡（5627点）集中在震中与记录最高地面加速度峰值的站点之间。数百次余震之后发生了重大震动。此外，在伊武里地区，未来发生地震的可能性很高。研究区域的可持续管理和减灾需要有效的预测和敏感性评估方法。这项研究的目的是评估基于深度神经网络的自动编码器框架的性能，以预测和诱发地震引发的区域滑坡。通过应用12种采样大小和12种滑坡影响因子，使用自动编码器框架制作了12个滑坡敏感性图。使用定性和定量评估方法评估模型的结果。从包括平原和山区（PM）的组合区域和仅山区的区域（M）的组合区域随机生成的非滑坡点上的抽样大小之比，影响了模型性能的不同预测能力。包括滑坡敏感性指数在内的12个磁化率图显示了PM和M中滑坡敏感性值的各种空间分布。高度精确的模型明确地将滑坡的潜在区域与稳定区域区分开，而没有扩大潜在区域的空间范围滑坡区。