Work

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

Year: 2020

Type: article

Abstract: 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.

Source: Jurnal Matematika Statistika dan Komputasi

DOI: 10.20956/jmsk.v17i2.11918

Authors: Amran Amran, Muh. Idil Islami, A. Kresna Jaya, Bambang Bakri

Institution: Hasanuddin University

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDGs: Climate action, Clean water and sanitation

Open Access status: diamond

OpenAlex ID: W3117086090

FWCI: 0.4

Cites: 27

Cited by: 4

Related to: 10