Landslide displacement prediction from on-site deformation data based on time series ARIMA model

作者：Wang Zhao, Jiakui Tang, Shengshan Hou, Yanjiao Wang, Anan Zhang, Jiru Wang, Wuhua Wang, Zhen Feng, Ang Li, Bing Han · 发表于：Frontiers in Environmental Science · 年份：2023 · DOI：10.3389/fenvs.2023.1249743 · 被引用次数：18 · 研究领域：Landslides and related hazards、Hydrological Forecasting Using AI、Remote Sensing and Land Use

Time series Autoregressive Integrated Moving Average (ARIMA) model is often used in landslide prediction and forecasting. However, few conditions have been suggested for the application of ARIMA models in landslide displacement 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 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-h landslide displacement data. The prediction results are RMSE = 4.52 mm and MAPE = 2.39%, and the prediction error increases gradually with time. Meanwhile, the ARIMA model was used to predict the 24-h displacements from initial deformation to initial acceleration deformation for the landslide in Guangna Township and the landslide in Libian Gully, and the prediction results were RMSE = 1.24 mm, MAPE = 1.34% and RMSE = 5.43 mm, 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 la...