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Hybrid state-space model and adjusting procedure based on Bayesian approaches for spatio-temporal rainfall disaggregation

机译:基于贝叶斯近日降雨分解的贝叶斯途径的混合状态空间模型及调整过程

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Disaggregation is the transforming process from highlevel scale data into low-level which preserves the consistency of the high-level statistic characteristics. This process, considering the dependence between spatial and temporal, is known as the spatio-temporal disaggregation. In general, this method is divided into two stages, namely the data modeling and preserving of consistency the high level scale statistic characteristics. This study proposes a hybrid model that combines a state-space model and adjusting procedure to disaggregate spatio-temporal rainfall through Bayesian approach using WinBUGS. The results show that the generated hourly rainfall data are consistent with the observed daily rainfall data at some locations which have only the daily rainfall data in the watershed Sampean, Bondowoso, Indonesia.
机译:分解是从高位尺度数据转换过程中的低级,保留了高级统计特征的一致性。考虑到空间和时间之间的依赖,称为时空分解。通常,该方法分为两个阶段,即数据建模和保持一致性的高级比例统计特征。本研究提出了一种混合模型,将状态空间模型和调整过程结合到通过使用WINBUGS通过贝叶斯方法分解时空降雨。结果表明,生成的每小时降雨数据与观察到的日常降雨数据一致,在某些地方只有日常降雨数据,在海滨山脉,邦多尼亚州邦多斯多邦多斯。

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