A comparison of GSTAR-SUR models and a hybrid GSTAR-SUR/neural network model on residuals of precipitation forecasting. Issue 9 (2nd September 2021) - Iriany, Atiek; Rosyida, Diana; Arifin, Arifin
Abstract: The Generalized Space Time Autoregressive-Seemingly Unrelated Regression (GSTAR-SUR) model is often used to forecast data that have time and location components. At the current time, precipitation is difficult to predict because it has patterns and characteristics that are hard to identify. This phenomenon is referred to as a non-linear phenomenon. One model that considers non-linearity is the neural network. The GSTAR-SUR model is a linear model, so at the current time is needed non-linear model on precipitation forecasting. This current research compares the precipitation forecast results from the GSTAR-SUR model and a hybrid GSTAR-SUR-with-neural-network approach in residuals. The data used in this research are the precipitation records for four locations in West Java for the years 2005 to 2015. The precipitation data represent 10-day-long observations. Precipitation in the four locations will be modeled using two approaches, i.e., GSTAR-SUR and GSTAR-SUR-NN. In the GSTAR-SUR-NN model, the residuals of the GSTAR-SUR model will be the basis of a neural network model. In this case, the GSTAR-SUR-NN model resulted in forecast data that are closer to the observed values than those from the GSTAR-SUR model. The mean forecasting error of the GSTAR-SUR-NN model was 3.8917 mm, while that of the GSTAR-SUR model was 4.3295 mm.