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Spatio-Temporal Model of Rainfall Data Using Kalman Filter and Expectation-Maximization Algorithm
MODEL SPATIO TEMPORAL DATA CURAH HUJAN MENGGUNAKAN KALMAN FILTER DAN ALGORITMA EKSPEKTASI-MAKSIMISASI
Jurnal Matematika, Statistika dan Komputasi
ISSN 2614-8811, 1858-1382
Published 2020-12-23
Publisher Department of Mathematics, Hasanuddin University
Language en
Abstract
English
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.
Bahasa Indonesia
Pemodelan data berdimensi lokasi dan waktu atau dikenal sebagai data spasial-temporal, umumnya memiliki kompleksitas yang tinggi. Penelitian ini menganalisis pola data curah hujan menggunakan model spasial-temporal dan hubungannya dengan variable iklim yakni temperatur dan kelembaban. Kompleksitas hubungan antara variabel dan parameter dalam model spasial-temporal disederhanakan dengan pendekatan hierarki. Estimasi parameter model spasio-temporal menggunakan pendekatan Kalaman Filter dan metode Ekspektasi-Maksimisasi (EM) yang dikombinasikan dengan metode bootstrap untuk menghitung standar eror penaksiran parameter. Implementasi model spasial-temporal pada data curah hujan di Provinsi Sulawesi Selatan dengan kovariat temperatur dan kelembaban udara menunjukkan bahwa terdapat hubungan antara curah hujan dengan temperatur dan kelembaban.
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Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai,
first harvested 5 Nov 2026,
last seen 5 Nov 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en">Spatio-Temporal Model of Rainfall Data Using Kalman Filter and Expectation-Maximization Algorithm</dc:title>
<dc:title xml:lang="id">MODEL SPATIO TEMPORAL DATA CURAH HUJAN MENGGUNAKAN KALMAN FILTER DAN ALGORITMA EKSPEKTASI-MAKSIMISASI</dc:title>
<dc:creator xml:lang="en">Amran, Amran</dc:creator>
<dc:creator xml:lang="en">Islami, Muh. Idil </dc:creator>
<dc:creator xml:lang="en"> Jaya, A. Kresna</dc:creator>
<dc:creator xml:lang="en">Bakri, Bambang </dc:creator>
<dc:subject xml:lang="en">Spatial-Temporal Model</dc:subject>
<dc:subject xml:lang="en">Kalman Filter</dc:subject>
<dc:subject xml:lang="en">Expectation-Maximization Algorithm</dc:subject>
<dc:subject xml:lang="en">Bootstrap</dc:subject>
<dc:subject xml:lang="en">Rainfall</dc:subject>
<dc:description xml:lang="en">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.</dc:description>
<dc:description xml:lang="id">Pemodelan data berdimensi lokasi dan waktu atau dikenal sebagai data spasial-temporal, umumnya memiliki kompleksitas yang tinggi. Penelitian ini menganalisis pola data curah hujan menggunakan model spasial-temporal dan hubungannya dengan variable iklim yakni temperatur dan kelembaban. Kompleksitas hubungan antara variabel dan parameter dalam model spasial-temporal disederhanakan dengan pendekatan hierarki. Estimasi parameter model spasio-temporal menggunakan pendekatan Kalaman Filter dan metode Ekspektasi-Maksimisasi (EM) yang dikombinasikan dengan metode bootstrap untuk menghitung standar eror penaksiran parameter. Implementasi model spasial-temporal pada data curah hujan di Provinsi Sulawesi Selatan dengan kovariat temperatur dan kelembaban udara menunjukkan bahwa terdapat hubungan antara curah hujan dengan temperatur dan kelembaban.</dc:description>
<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
<dc:date>2020-12-23</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/11918</dc:identifier>
<dc:identifier>10.20956/jmsk.v17i2.11918</dc:identifier>
<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 17 No. 2 (2021): JANUARY 2021; 304-313</dc:source>
<dc:source>2614-8811</dc:source>
<dc:source>1858-1382</dc:source>
<dc:language>en</dc:language>
<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/11918/6180</dc:relation>
<dc:relation>10.20956/j.v17i2.11918.g6180</dc:relation>
<dc:rights xml:lang="en">Copyright (c) 2020 Author and publisher</dc:rights>
<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>
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Cite this article
APA 7
Amran, A., Islami, M. I., Jaya, A. K., & Bakri, B. (2020). Spatio-Temporal Model of Rainfall Data Using Kalman Filter and Expectation-Maximization Algorithm. Jurnal Matematika, Statistika dan Komputasi, 17(2), 304–313. https://doi.org/10.20956/jmsk.v17i2.11918
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IEEE
A. Amran, M. I. Islami, A. K. Jaya, and B. Bakri, “Spatio-Temporal Model of Rainfall Data Using Kalman Filter and Expectation-Maximization Algorithm,” Jurnal Matematika, Statistika dan Komputasi, vol. 17, no. 2, pp. 304–313, 2020, doi: 10.20956/jmsk.v17i2.11918.
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