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Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning

Aptisi Transactions on Technopreneurship (ATT) · Vol. 5 No. 1Sp (2023) · hlm. 109–123

DOI
10.34306/att.v5i1Sp.337
ISSN
2656-8888, 2655-8807
Terbit
2023-08-21
Penerbit
Pandawan
Bahasa
en
Lisensi
creativecommons.org/licenses/by/4.0

Abstrak

Air quality has become a major public concern due to the significant threat posed by air pollution to human health, and rapid and efficient monitoring of air quality is crucial for pollution control and human health. In this paper, deep learning and image-based models are proposed to estimate air quality. To evaluate the level of air quality, the model collects feature information from landscape photos taken by mobile cameras. To analyze public perception of air quality, researchers collected questionnaire data from 257 people. The Smartpls method allows for structural analysis to determine the influence of each variable on other variables and the extent of their contribution to the final variable of overall perception of air quality. This study aims to develop a novel approach for air quality prediction using image-based data and machine learning techniques. The research used convolutional neural networks to extract features from images and predict the air quality index. The study was conducted using a dataset obtained from a network of air quality sensors across the city. The results of the study showed that the proposed approach can provide accurate air quality predictions compared to the traditional methods. The developed model was able to capture the complex relationships between air quality and environmental factors, such as temperature and humidity. The implications of the study suggest that image-based air quality prediction can be a powerful tool for improving public health and reducing the impact of air pollution. The study's findings hold promise for a healthier future by facilitating more effective pollution management and improved air quality regulation. The study's primary novelty lies in its approach to air quality prediction by deploying convolutional neural networks to extract image features for predicting air quality indices. This application of advanced machine learning techniques to image-based data for air quality estimation marks a significant advancement.

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Ihsan Nuril HikamAfiliasi tidak diketahui3
Tuti NurhaeniAfiliasi tidak diketahui2
Alfri AdiwijayaAfiliasi tidak diketahui2
Yamato ShinoAfiliasi tidak diketahui2
Ninda LutfianiUniversitas Raharja2
Dewi ImmaniarAfiliasi tidak diketahui2
Qurotul AiniUniversitas Raharja2
Untung RahardjaAfiliasi tidak diketahui2
Po Abas SunaryaAfiliasi tidak diketahui2
Muhamad YusupAfiliasi tidak diketahui2
Ankur SinghAfiliasi tidak diketahui1
Widya FebrianiAfiliasi tidak diketahui1
Nuke Puji Lestari SantosoAfiliasi tidak diketahui1
Amitkumar DudhatAfiliasi tidak diketahui1
Husni Teja SukmanaAfiliasi tidak diketahui1
Aditya MardiansyahAfiliasi tidak diketahui1
Nur AzizahUniversitas Raharja1
Dedeh SupriyantiAfiliasi tidak diketahui1
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Hilda HildaAfiliasi tidak diketahui1
Aan KaniviaAfiliasi tidak diketahui1
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Tri HartonoAfiliasi tidak diketahui1
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Lukita PashaAfiliasi tidak diketahui1
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Semaria Eva Elita GirsangAfiliasi tidak diketahui1
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Santiago RamirezAfiliasi tidak diketahui1
Goh YaoAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://att.aptisi.or.id/index.php/att/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 6 Nov 2026.
Metadata asli (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">Image-based Air Quality Prediction using Convolutional Neural Networks and Machine Learning</dc:title>
	<dc:creator xml:lang="en">Hardini, Marviola</dc:creator>
	<dc:creator xml:lang="en">Riza Chakim, Mochamad Heru </dc:creator>
	<dc:creator xml:lang="en">Magdalena, Lena </dc:creator>
	<dc:creator xml:lang="en">Kenta, Hiroshi </dc:creator>
	<dc:creator xml:lang="en">Rafika, Ageng Setiani </dc:creator>
	<dc:creator xml:lang="en">Julianingsih, Dwi</dc:creator>
	<dc:subject xml:lang="en">Air Quality</dc:subject>
	<dc:subject xml:lang="en">AIR-Protection</dc:subject>
	<dc:subject xml:lang="en">Convolutional Neural Networks</dc:subject>
	<dc:subject xml:lang="en">Machine Learning</dc:subject>
	<dc:subject xml:lang="en">Smartpls</dc:subject>
	<dc:description xml:lang="en">Air quality has become a major public concern due to the significant threat posed by air pollution to human health, and rapid and efficient monitoring of air quality is crucial for pollution control and human health. In this paper, deep learning and image-based models are proposed to estimate air quality. To evaluate the level of air quality, the model collects feature information from landscape photos taken by mobile cameras. To analyze public perception of air quality, researchers collected questionnaire data from 257 people. The Smartpls method allows for structural analysis to determine the influence of each variable on other variables and the extent of their contribution to the final variable of overall perception of air quality. This study aims to develop a novel approach for air quality prediction using image-based data and machine learning techniques. The research used convolutional neural networks to extract features from images and predict the air quality index. The study was conducted using a dataset obtained from a network of air quality sensors across the city. The results of the study showed that the proposed approach can provide accurate air quality predictions compared to the traditional methods. The developed model was able to capture the complex relationships between air quality and environmental factors, such as temperature and humidity. The implications of the study suggest that image-based air quality prediction can be a powerful tool for improving public health and reducing the impact of air pollution. The study's findings hold promise for a healthier future by facilitating more effective pollution management and improved air quality regulation. The study's primary novelty lies in its approach to air quality prediction by deploying convolutional neural networks to extract image features for predicting air quality indices. This application of advanced machine learning techniques to image-based data for air quality estimation marks a significant advancement.</dc:description>
	<dc:publisher xml:lang="en">Pandawan</dc:publisher>
	<dc:date>2023-08-21</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/337</dc:identifier>
	<dc:identifier>10.34306/att.v5i1Sp.337</dc:identifier>
	<dc:source xml:lang="en">Aptisi Transactions on Technopreneurship (ATT); Vol. 5 No. 1Sp (2023): Special Issue: Technopreneurship Driving Change in the Nation's Future Leadership; 109-123</dc:source>
	<dc:source xml:lang="id">Aptisi Transactions On Technopreneurship (ATT); Vol 5 No 1Sp (2023): Special Issue: Technopreneurship Driving Change in the Nation's Future Leadership; 109-123</dc:source>
	<dc:source>2656-8888</dc:source>
	<dc:source>2655-8807</dc:source>
	<dc:source>10.34306/att.v5i1Sp</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://att.aptisi.or.id/index.php/att/article/view/337/223</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Dwi Julianingsih, Marviola Hardini, Mochamad Heru  Riza Chakim, Lena  Magdalena, Hiroshi  Kenta, Ageng Setiani  Rafika</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>