Evaluating the Effectiveness of Exponentially Weighted Moving Average Filter in Enhancing Landslide Detection from Accelerometer Data

Landslides are a major natural hazard that can cause significant damage to infrastructure, loss of life, and economic disruption. Early detection of landslides can greatly reduce the impact of these events and is crucial for effective risk management and mitigation. In this study, we propose a method of landslide detection using accelerometer-based soil movement sensing and exponentially weighted moving average (EWMA) digital filtering. The accelerometer data are processed using exponentially weighted moving average digital filtering to reduce the noise in the data, which is then used to detect ground motion. The results demonstrate a statistically significant improvement with the use of the EWMA method, as indicated by the reduced root-mean-square error (RMSE) values. Specifically, the raw data RMSE was 0.1103, whereas the EWMA RMSE was 0.0331. The standard deviation of the raw data was 0.1072, while that of the EWMA was 0.0201. The proposed method has the potential to improve the early warning capabilities of landslide monitoring systems and enhance the safety of people living in landslide-prone areas.