HYBRID MODEL GSTAR-SUR-NN FOR PRECIPITATION DATA
ABSTRACT: 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.
Author: Agus Dwi Sulistyono,
Waego Hadi Nugroho, Rahma Fitriani, Atiek Iriani
Journal Code: jpmatematikagg160036
