Prediction of Landsliding using Univariate Forecasting Models

In last few decades, many methods are proposed for time-series forecasting. As always, when alternatives exists, choice needs to be made so that an appropriate forecasting method can be selected, and used for a specific forecasting. Primarily, the type of data used for time-series forecasting are univariate and multivariate. In this paper, we presented an analysis of univariate time-series forecasting data using ARIMA, GARCH and Dynamic Neural Network (DNN) modeling techniques. These techniques depend on a variety of parameters such as objective of forecasting, type of forecasted data and whether an automatic or manual approach is to be used for forecasting. We implemented proposed methods for 15 m landslide sensor data. The objective of the paper is to find a best method among well-known techniques for landslide forecasting. The obtained results validate that by implementing three different models, DNN is best-in-class for time-series landslide forecasting.