T177 Current Trends in Landslide Inventories and Assessments: Innovative Techniques, Collection Methods, Modeling, and Risk (Posters)

Tuesday, 24 September 2024
8:00 AM - 5:30 PM
Hall D

LANDSLIDE DISPLACEMENT PREDICTION FROM ON-SITE DEFORMATION DATA BASED ON TIME SERIES ARIMA MODEL

Paper number: T177-1
Program: Technical Sessions
Time: 8:00 AM – 5:30 PM

Authors

  • Shengshan Hou Presenting Author
    China Institute of Geo-Environment monitoring, Beijing, Beijing
  • Bing Han
    China Institute of Geo-Environment monitoring, Beijing

Abstract

The prediction and forecasting of landslide disaster is a world-class problem recognized by the international researchers. Time series ARIMA model is often used in landslide prediction and forecasting. However, few applicable conditions have been proposed for the application of ARIMA model in landslide prediction and prediction. This paper summarizes the distribution law of the tangential angle in different time periods and analyzes the landslide displacement data by combining wavelet transform. It proposes an applicable condition for the ARIMA model in the field of landslide deformation prediction: when the landslide deformation is in the initial deformation to initial acceleration stage, i.e., the tangential angle of landslide displacement is less than 80°, the ARIMA model has higher prediction accuracy for 24-hour landslide displacement data. The prediction results are RMSE=4.52mm and MAPE=2.39%, and the prediction error increases gradually with time. The ARIMA model was also used to predict the displacement from the initial deformation to the initial acceleration of the landslide in the Libian Gully for 24 hours, and the prediction results were RMSE=5.43mm and MAPE=1.67%, which still maintained high accuracy and thus verified this applicable condition. At the same time, taking the landslide of Libian Gully as an example, the ARIMA model was used to test the displacement prediction effect of the landslide in the Medium-term acceleration stage and the Imminent sliding stage (the tangential angle of landslide displacement is 80° and 85°, respectively). The relative error of displacement data prediction in the Medium-term acceleration stage is within 3%, while the relative error of the prediction value in the Imminent sliding stage is more than 3%, and the error gradually increases with time. This demonstrates that the relative error of the ARIMA model in landslide prediction and forecasting is within 3%. The relative error of the prediction value in the Imminent sliding stage is above 3%, and the error increases gradually with time. The applicable conditions proposed in this study can provide a reference for the application of ARIMA model in landslide prediction and forecast.

Geological Society of America Abstracts with Programs. Vol. 56, No. 5

doi: 10.1130/abs/2024AM-400525

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