A framework for identifying the onset of landslide acceleration based on the exponential moving average (EMA)

Predicting the failure time of a landslide is considered as challenging work in the field of landslide research, and inverse velocity is proved to be an effective and convenient method. The onset of acceleration (OOA) has a crucial effect on the prediction failure time from the inverse velocity method. However, a simple method to identify OOA points is lacked, and most of the identifications rely on expert experience. Therefore, this study presents an application of a simple framework developed to identify the OOA by analyzing monitoring velocity data in three steps, including selection of the absolute value of velocity, reliable area identification and OOA identification. A new parameter based on exponential moving average (EMA) is developed to identify the landslide OOA. The framework is applied to three historical case studies to test its practicability and effectiveness. The forecasting results show a good correspondence between the accuracy rate and the coefficient of determination (R-2). The predicted failure time according to the linear extrapolation starting from the identified OOA points is acceptable with a high R-2 and high accuracy.

Automated summary

This study presents a simple framework for identifying the onset of acceleration (OOA) in landslides by analyzing monitoring velocity data. The framework includes three steps: selection of the absolute value of velocity, reliable area identification, and OOA identification. A new parameter based on exponential moving average (EMA) is developed to identify the landslide OOA. The framework is applied to three historical case studies, demonstrating good correspondence between the accuracy rate and the coefficient of determination (R-2). The predicted failure time based on the identified OOA points shows high R-2 and accuracy.