Spatio-Temporal Model of Rainfall Data Using Kalman Filter and Expectation-Maximization Algorithm | Zendy

Amran Amran | Zendy; Muh. Idil Islami | Zendy; A K Jaya | Zendy; Bambang Bakri | Zendy

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Spatio-Temporal Model of Rainfall Data Using Kalman Filter and Expectation-Maximization Algorithm

Author(s) -

Amran Amran,

Muh. Idil Islami,

A K Jaya,

Bambang Bakri

Publication year - 2020

Publication title -

jurnal matematika statistika dan komputasi

Language(s) - English

Resource type - Journals

eISSN - 2614-8811

pISSN - 1858-1382

DOI - 10.20956/jmsk.v17i2.11918

Subject(s) - kalman filter , dimension (graph theory) , expectation–maximization algorithm , temporal database , humidity , maximization , computer science , algorithm , statistics , meteorology , data mining , mathematics , maximum likelihood , geography , mathematical optimization , pure mathematics

Location and time dimension data modeling, also known as spatial-temporal data, generally has high complexity. This study analyzes a spatial-temporal model of rainfall data and climate variables, namely temperature, and humidity. The complexity of the relationship between variables and parameters in the spatial-temporal model is simplified by a hierarchical approach. The parameter estimation of the ratio-temporal model uses the Kalman Filter approaches and the Expectation-Maximization (EM) method combined with the bootstrap method to calculate the standard error estimation. Implementation of the spatial-temporal model on rainfall data in South Sulawesi Province with temperature and humidity shows that there is a relationship between rainfall and temperature and humidity.

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