EGU23-1702, updated on 23 Feb 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Rainfall-induced Landslide temporal probability prediction and meteorological early warning modeling based on LSTM_TCN model

yu Zhao and lixia Chen
yu Zhao and lixia Chen
  • China University of Geosciences, Wuhan, School of Geophysics and Geomatics, China (

Abstract: The occurrence time of investigated landslide hazard is not complete, leading to an error in the statistical relationship between rainfall and landslide. And the low accuracy of the critical rainfall threshold model will be built. And further, it will lead to an increase in the false positive rate of meteorological early warning. This study takes rainfall-induced landslides in the Wanzhou District of Chongqing from 1995 to 2015 as the research object. And Henghe Township, where historical disaster data is missing seriously, is the verification area. This study proposes a prediction model of the daily temporal probability of landslides occurrence on a certain day based on Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN). The method is used to reconstruct the temporal information of rainfall-induced landslide events by simulating the nonlinear relationship between the occurrence time of landslides and rainfall. The landslide events after the reconstruction of temporal information were verified and selected, and then applied to the reasonable division of the E-D effective rainfall threshold curve, so as to establish the landslide meteorological warning model. The average temporal probability of rainfall-induced landslide occurrence on a certain day predicted by the proposed method reached 90.33%, which is higher than that of ANN (71.17%), LSTM (72.75%), and TCN (86.91%). Based on the temporal probability of landslide occurrence on a certain day which is higher than the 90% probability threshold, 18-time information including 42 landslides in Henghe Township of the verification area is expanded to 201. Compared with only using the historical landslide events, the meteorological warning model based on the expanded time information has a more reasonable warning classification, and the effective warning rate in the severe warning level is increased by 42.86%. The model method in this study is of constructive significance to the daily temporal probability prediction of rainfall-induced landslides on the regional scale and is helpful for the government to accurately model the risk decision of landslide meteorological warning.

How to cite: Zhao, Y. and Chen, L.: Rainfall-induced Landslide temporal probability prediction and meteorological early warning modeling based on LSTM_TCN model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1702,, 2023.

Supplementary materials

Supplementary material file