Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 3

BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 2 (2024) · pp. 1105–1116

DOI
10.30598/barekengvol18iss2pp1105-1116
ISSN
2615-3017, 1978-7227
Published
2024-05-25
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.

Collaboration network of the authors

40 authors · 135 co-authorship links · 5 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • Universitas Widya Kartika
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ani Budi AstutiUniversity of Brawijaya3
Henny PramoedyoUniversity of Brawijaya2
Adji Achmad Rinaldo FernandesUniversity of Brawijaya2
Sri HandayaniAffiliation unknown2
Alifiandi Rafi MuhammadAffiliation unknown2
Ulfah MaisarohAffiliation unknown2
Darmanto DarmantoUniversitas Widya Kartika2
Nurjannah NurjannahInstitut Agama Islam Negeri Kendari2
Isnani DartiAffiliation unknown2
Muhammad Hisyam LeeAffiliation unknown2
Rismania Hartanti Putri Yulianing DamayantiAffiliation unknown2
Husnul KhatimahAffiliation unknown2
Ni Wayan Surya WardhaniAffiliation unknown2
Adinda Gita MaharaniAffiliation unknown2
Atmadani Rahayu FakhrunnisaAffiliation unknown2
Silvia Intan AuliaAffiliation unknown2
Alya Fitri SyalsabillaAffiliation unknown1
Alya Fitri SyalsabillaUniversity of Brawijaya1
Agus Fachrur RozyUniversity of Brawijaya1
Dwi Ayu LusiaUniversity of Brawijaya1
Heni KusdarwatiUniversity of Brawijaya1
Imelda SalsabilaAffiliation unknown1
Achmad EfendiAffiliation unknown1
Fachri FaisalAffiliation unknown1
Atiek IrianyUniversity of Brawijaya1
Petrus Kanisius OlaAffiliation unknown1
Rahma FitrianiAffiliation unknown1
Armando Jacquis Federal ZamelinaAffiliation unknown1
Lucius RamifidisoaAffiliation unknown1
Solimun SolimunUniversity of Brawijaya1
Usriatur RohmaAffiliation unknown1
Achmad RifaiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA</dc:title>
	<dc:creator>Astutik, Suci</dc:creator>
	<dc:creator>Rahmi, Nur Silviyah</dc:creator>
	<dc:creator>Irsandy, Diego</dc:creator>
	<dc:creator>Saniyawati, Fang You Dwi Ayu Shalu</dc:creator>
	<dc:creator>Mashfia, Fidia Raaihatul</dc:creator>
	<dc:creator>Lusiana, Evelin Dewi</dc:creator>
	<dc:creator>Risda, Intan Fadhila</dc:creator>
	<dc:creator>Susanto, Mohammad Hilmi</dc:creator>
	<dc:description xml:lang="en-US">Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-05-25</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/11917</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss2pp1105-1116</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application; 1105-1116</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss2year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/11917/7911</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Suci Astutik, Nur Silviyah Rahmi, Diego Irsandy, Fang You Dwi Ayu Shalu Saniyawati, Fidia Raaihatul Mashfia, Evelin Dewi Lusiana, Intan Fadhila Risda, Mohammad Hilmi Susanto</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>