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The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5

Aptisi Transactions on Technopreneurship (ATT) · Vol. 4 No. 3 (2022) · pp. 313–324

DOI
10.34306/att.v4i3.282
ISSN
2656-8888, 2655-8807
Published
2022-10-31
Publisher
Pandawan
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables.

Keywords

Collaboration network of the authors

40 authors · 163 co-authorship links · 14 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.

  • Universitas Raharja
  • Satya Wacana Christian University
  • Universitas Buddhi Dharma
  • Universitas Catur Insan Cendekia Cirebon
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Irwan SembiringSatya Wacana Christian University17
Ninda LutfianiUniversitas Raharja14
Nuke Puji Lestari SantosoUniversitas Raharja12
Alfiah KhoirunisaUniversitas Raharja8
Shofiyul MillahAffiliation unknown7
Nur AzizahUniversitas Raharja5
Agung RizkyAffiliation unknown5
Sutarto WijonoAffiliation unknown5
Dwi ApriliasariAffiliation unknown5
Adam FaturahmanAffiliation unknown5
Untung RahardjaUniversitas Raharja4
Ade IrianiSatya Wacana Christian University4
Hindriyanto Dwi PurnomoSatya Wacana Christian University4
Ihsan Nuril HikamAffiliation unknown4
Marviola HardiniAffiliation unknown4
Yulia Putri Ayu SanjayaAffiliation unknown4
Muhtarom MuhtaromAffiliation unknown3
Nesti Anggraini SantosoAffiliation unknown3
Natasya Aprila YusufAffiliation unknown3
Alwiyah AlwiyahAffiliation unknown3
Nuke Puji Lestari SantosoAffiliation unknown3
Desy AprianiAffiliation unknown2
Mohamad RakhmansyahAffiliation unknown2
Lena MagdalenaAffiliation unknown2
Umi NarimawatiAffiliation unknown2
Fitra Putri OgandaUniversitas Raharja2
Hendry HendrySatya Wacana Christian University2
Tarisya RamadhanAffiliation unknown2
Nikita IvanovAffiliation unknown2
Raihan RaihanAffiliation unknown2
Mochamad Heru Riza ChakimAffiliation unknown2
Chandra LukitaUniversitas Catur Insan Cendekia Cirebon2
Nanda SeptianiAffiliation unknown2
Fauziyyah Husna Nurdiayanah LestariAffiliation unknown2
Benny DaniawanUniversitas Buddhi Dharma1

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://att.aptisi.or.id/index.php/att/oai, first harvested 6 Nov 2026, last seen 6 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2.5</dc:title>
	<dc:creator xml:lang="en">Rahardja, Untung </dc:creator>
	<dc:creator xml:lang="en">Aini, Qurotul </dc:creator>
	<dc:creator xml:lang="en">Sunarya, Po Abas </dc:creator>
	<dc:creator xml:lang="en">Manongga, Danny </dc:creator>
	<dc:creator xml:lang="en">Julianingsih, Dwi</dc:creator>
	<dc:subject xml:lang="en">-</dc:subject>
	<dc:description xml:lang="en"> Artificial intelligence techniques to forecasts based on the Community Multiscale Air Quality (PM2.5) operational model can be known using TensorFlow. TensorFlow was used in this study to assess the scores of the Recurrent Neural Networks (RNN) input variables on the 6-hour forecast for July-October 2022. The relevance scores for the one- and two-day forecasts are represented by the sum of the relevance scores across the target prediction timeframe 2–5 and 4–7 previous time steps. The initial selection of input variables was based on their correlation coefficient with the measured PM2.5 concentration. Still, the order of contribution of the input variables measured by TensorFlow was different from the order of their correlation coefficients, which indicated an inconsistency between the linear and nonlinear variables of the method. It was found that the retraining of the RNN model using a subset of variables with a high relevance score resulted in a predictive ability similar to the initial set of input variables. </dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2022-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://att.aptisi.or.id/index.php/att/article/view/282</dc:identifier>
	<dc:identifier>10.34306/att.v4i3.282</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 4 No. 3 (2022): November; 313-324</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 4 No 3 (2022): November; 313-324</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v4i3</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/282/188</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2022 Untung  Rahardja, Qurotul  Aini, Po Abas  Sunarya, Danny  Manongga, Dwi Julianingsih</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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