Abstract
This research is a development from previous research that has studied the method of spatio temporal disaggregation with State space and adjusting procedures for predicting hourly rainfall based on daily rainfall (Astutik et al, 2013). However, this study is limited to predicting hourly rainfall in some sampled locations in the future. Astutik et al (2017, 2018) have modeled hourly and daily rainfall using posterior predictive bayesian VAR at the Sampean watershed of Bondowoso. This study aims to predict hourly rainfall data based on daily rainfall data in the future at the outsampled locations using posterior predictive bayesian VAR and adjusting procedures in the method of spatio temporal disaggregation.
Highlights
The method of spatio temporal disaggregation is a method for prediction or generating low scale data from high-scale data involving time and location information
Koutsoyiannis et al (2003); Bojilova, 2004; Segond et al, 2006, 2007 have developed a spatio temporal disaggregation method to generate synthetic data involving two resolutions / time scales which require that low scale data synthetic series must be consistent with high scale data observation series involving location information and time
Low time scale generation data is carried out through the BVAR (5) model which has been estimated in the previous stage (Astutik et al, 2018)
Summary
The method of spatio temporal disaggregation is a method for prediction or generating low scale (hourly) data from high-scale (daily) data involving time and location information. There are two stages in the disaggregation method, namely (1) modeling of low time scale rainfall data (eg hourly data) and high time scales (eg daily data) and (2) maintaining the consistency of low time scale rainfall data with high time scales. Koutsoyiannis et al (2003); Bojilova, 2004; Segond et al, 2006, 2007 have developed a spatio temporal disaggregation method to generate synthetic (prediction) data involving two resolutions / time scales (high and low time scales) which require that low scale data synthetic series must be consistent with high scale data observation series involving location information (space) and time.
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