Adaptive classification of landslide displacement evolution states through multi-landslide data transfer learning

Xuhuang Du (), Cheng Lian (), Youping Li (), Zhiyong Qi (), Zhengyang Tang (), Jin Yuan (), Bo Xu () and Hui Zeng ()
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Xuhuang Du: Hubei Technology Innovation Center for Smart Hydropower
Cheng Lian: Wuhan University of Technology
Youping Li: Hubei Technology Innovation Center for Smart Hydropower
Zhiyong Qi: Hubei Technology Innovation Center for Smart Hydropower
Zhengyang Tang: Hubei Technology Innovation Center for Smart Hydropower
Jin Yuan: Hubei Technology Innovation Center for Smart Hydropower
Bo Xu: Hubei Technology Innovation Center for Smart Hydropower
Hui Zeng: China Yangtze Power Co., Ltd. (CYPC)

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 10, No 24, 11915-11930

Abstract: Abstract The adaptive identification of the current evolution state of landslides through the analysis of landslide displacement time series data using advanced machine learning algorithms is of significant research importance. This paper proposes an advanced landslide displacement evolution state classification model based on clustering, transfer learning, and deep neural networks. The first step involves clustering analysis of the variation of landslide displacement, where landslide displacement subsequences obtained through slicing are labeled with evolution state category labels.The second step involves pre-training the deep learning model on multiple landslide displacement datasets to capture the inherent unified representations of different landslide displacement data. The third step involves fine-tuning the deep learning model on a specific landslide displacement dataset to capture the intrinsic features of the specific landslide displacement data. We validate the effectiveness of the proposed method on six landslide datasets located in China. The experimental results show that the proposed method can improve the accuracy of landslide evolution state classification on four popular deep learning models.

Keywords: Landslide evolution state classification; Time series classification; Transfer learning; Clustering analysis. (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07266-4

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