Rainfall Forecasting in Agricultural Areas Using GSTAR-SUR-NN Model
Agus Dwi Sulistyono, Hartawati, Ni Wayan Suryawardhani, Atiek Iriany, Aniek Iriany
Faculty of Fisheries and Marine Science, Brawijaya University
Department of Statistics Faculty of Mathematics and Natural Sciences, Brawijaya University
Department of Agrotechnology, Faculty of Agriculture and Animal Husbandry, University of Muhammadiyah Malang
Abstract
The use of location weights on the formation of the spatio-temporal model contributes to the accuracy of the model formed. The location weights that are often used include uniform location weight, inverse distance, and normalization of cross-correlation. The weight of the location considers the proximity between locations. For data that has a high level of variability, the use of the location weights mentioned above is less relevant. This research was conducted with the aim of obtaining a weighting method that is more suitable for data with high variability. This research was conducted using secondary data derived from 10 daily rainfall data obtained from BMKG Karangploso. The data period used was January 2005 to December 2015. The points of the rain posts studied included the rain post of the Blimbing, Karangploso, Singosari, Dau, and Wagir regions. Based on the results of the research forecasting model obtained is the GSTAR ((1), 1,2,3,12,36) -SUR model. The cross-covariance model produces a better level of accuracy in terms of lower RMSE values and higher R2 values, especially for Karangploso, Dau, and Wagir areas.
Keywords: cross-covariance, GSTAR Model, precipitation, spatio-temporal
Topic: International Conference on Sustainable Agriculture