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Landslide data analysis using various time-series forecasting models
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DOI：10.1016/j.compeleceng.2020.106858.png)
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Landslides are among the many devastating natural calamities that cause damage to life and property. Predicting landslides is an important task to enable preventive measures to be made on time. This paper presents an analysis of univariate time-series forecasting data using an auto regressive integrated moving average (ARIMA) model, a generalized autoregressive conditional heteroskedasticity (GARCH) model, and a dynamic neural network (DNN) model. These techniques rely on the objective of the forecasting, the type of forecasted data, and whether an automatic or manual approach is to be used for forecasting. Different techniques were analyzed on 15-meter landslide sensor data. The objective of this paper is to suggest a best method among well-known models for landslide forecasting. The demonstrated result shows that a dynamic neural network model is best in class for time-series landslide forecasting. Furthermore, upon objectively evaluating the three well-known techniques, the DNN model exhibited a minimum error rate of approximately 0.01 in comparison to other implemented techniques.
Keyword:
Time series forecasting
ARIMA
GARCH
Dynamic neural network
Landslide data
Data analysis
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