Open AccessSpatio-Temporal Model of Rainfall Data Using Kalman Filter and Expectation-Maximization Algorithm
Author(s) -
Amran Amran,
Muh. Idil Islami,
Andi Kresna Jaya,
Bambang Bakri
Publication year - 2020
Publication title -
jurnal matematika, statistika dan komputasi/jurnal matematika statistik 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.