Hybrid Model GSTAR-SUR-NN For Precipitation Data
Spatio-temporal model that have been developed such as Space-Time Autoregressive (STAR) model, Generalized Space-Time Autoregressive (GSTAR), GSTAR-OLS and GSTAR-SUR. Besides spatio-temporal phenomena, in daily life, we often find nonlinear phenomena, uncommon patterns and unidentified characteristics of the data. One of current developed nonlinear model is a neural network. This study is conducted to form a hybrid model GSTAR-SUR-NN to develop spatio-temporal model that has better prediction. This research is conducted on ten-daily rainfall data at 2005 - 2015 for Blimbing, Singosari, Karangploso, Dau, and Wagir region. Based on the results of this research, indicated that the accuracy of GSTAR ((1), 1,2,3,12,36)-SUR model used cross-covariance weight has relatively similar to GSTAR ((1), 1,2,3 , 12.36)-SUR-NN (25-14-5) for Blimbing and Singosari region with 5% error level. While Karangploso, Dau, and Wagir, GSTAR ((1), 1,2,3,12,36)-SUR-NN (25-14-5) model has better accuracy in predicting the precipitation at three locations with the value of R2prediction for each location is 0.992, 0.580, and 0.474.