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

Marviola Hardini; Mochamad Heru  Riza Chakim; Lena  Magdalena; Hiroshi  Kenta; Ageng Setiani  Rafika; Dwi Julianingsih

doi:10.34306/att.v5i1Sp.337

Authors

Marviola Hardini University of Raharja
Mochamad Heru Riza Chakim University of Raharja
Lena Magdalena University of Catur Insan Cendekia
Hiroshi Kenta University of Miyazaki
Ageng Setiani Rafika University of Raharja
Dwi Julianingsih Univeristy of Raharja

DOI:

https://doi.org/10.34306/att.v5i1Sp.337

Keywords:

Air Quality, AIR-Protection, Convolutional Neural Networks, Machine Learning, Smartpls

Abstract

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.

References

M. curic, O. Zafirovski, V. Spiridonov, M. curic, O. Zafirovski, and V. Spiridonov, “Air quality and health,” Essentials Med. Meteorol., pp. 143–182, 2022.

N. Singh, S. Singh, and R. K. Mall, “Urban ecology and human health: implications of urban heat island, air pollution and climate change nexus,” in Urban Ecology, Elsevier, 2020, pp. 317–334.

I. Manisalidis, E. Stavropoulou, A. Stavropoulos, and E. Bezirtzoglou, “Environmental and health impacts of air pollution: a review,” Front. public Heal., p. 14, 2020.

U. Rahardja, Q. Aini, P. A. Sunarya, D. Manongga, and D. Julianingsih, “The use of tensorflow in analyzing air quality artificial intelligence predictions pm2. 5,” Aptisi Trans. Technopreneursh., vol. 4, no. 3, pp. 313–324, 2022.

B. Surya et al., “The complexity of space utilization and environmental pollution control in the main corridor of Makassar City, South Sulawesi, Indonesia,” Sustainability, vol. 12, no. 21, p. 9244, 2020.

W. H. Organization, “WHO global air quality guidelines: particulate matter PM2. 5 and PM10, ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide: executive summary,” 2021.

M. Shen, W. Huang, M. Chen, B. Song, G. Zeng, and Y. Zhang, “Micro plastic crisis: un-ignorable contribution to global greenhouse gas emissions and climate change,” J. Clean. Prod., vol. 254, p. 120138, 2020.

T. Handra and V. P. K. Sundram, “The Effect of Human Resource Information Systems HRIS and Artificial Intelligence on Defense Industry Performance,” IAIC Trans. Sustain. Digit. Innov., vol. 4, no. 2, pp. 155–163, 2023.

J. Kujawska, M. Kulisz, P. Oleszczuk, and W. Cel, “Machine Learning Methods to Forecast the Concentration of PM10 in Lublin, Poland,” Energies, vol. 15, no. 17, p. 6428, 2022.

R. Bhandari and M. V. A. Sin, “Optimizing digital marketing in hospitality industries,” Startupreneur Bisnis Digit. (SABDA Journal), vol. 2, no. 1, 2023.

L. Rozensky, M. Hajek, Z. Vrba, R. Pokorný, J. Hansen, and J. Lipa, “An analysis of renewable energy consumption efficiency in terms of greenhouse gas production in selected European countries,” BioResources, vol. 15, no. 4, p. 7714, 2020.

T. Jamal, M. Zahid, J. M. Martins, M. N. Mata, H. U. Rahman, and P. N. Mata, “Perceived green human resource management practices and corporate sustainability: Multigroup analysis and major industries perspectives,” Sustainability, vol. 13, no. 6, p. 3045, 2021.

T. F. Rukuni and E. T. Maziriri, “Data on corona-virus readiness strategies influencing customer satisfaction and customer behavioural intentions in South African retail stores,” Data Br., vol. 31, p. 105818, 2020.

H. Fuadah and H. Setiyawati, “The Effect of the implementation of transparency and accounting information systems on the quality of financial reports,” IJO-International J. Bus. Manag. (ISSN 2811-2504), vol. 3, no. 11, pp. 1–12, 2020.

F. Hellweg, S. Lechtenberg, B. Hellingrath, and A. M. T. Thome, “Literature review on maturity models for digital supply chains,” Brazilian J. Oper. Prod. Manag., vol. 18, no. 3, pp. 1–12, 2021.

I. Bernardo, “Research on the Intention to Purchase of Fabric Saints: Based on the Theory of Consumption Value, Green Purchase Intention, and Green Purchase Behaviour,” Aptisi Trans. Technopreneursh., vol. 5, no. 1, pp. 31–39, 2023.

H. Ohmaid, S. Eddarouich, A. Bourouhou, and M. Timouyas, “Iris segmentation using a new unsupervised neural approach,” IAES Int. J. Artif. Intell., vol. 9, no. 1, p. 58, 2020.

X. Xu, H. Yang, and C. Li, “Theoretical model and actual characteristics of air pollution affecting health cost: a review,” Int. J. Environ. Res. Public Health, vol. 19, no. 6, p. 3532, 2022.

A. Williams, C. S. Bangun, and Y. Shino, “The Urgency of Digital Literacy in Indonesia on COVID-19 pandemic,” Startupreneur Bisnis Digit. (SABDA Journal), vol. 1, no. 2, pp. 183–190, 2022.

X. Ma, X. Li, M.P. Kwan, and Y. Chai, “Who could not avoid exposure to high levels of residence-based pollution by daily mobility Evidence of air pollution exposure from the perspective of the neighborhood effect averaging problem NEAP,” Int. J. Environ. Res. Public Health, vol. 17, no. 4, p. 1223, 2020.

X. Liu, D. Lu, A. Zhang, Q. Liu, and G. Jiang, “Data-driven machine learning in environmental pollution Gains and problems,” Environ. Sci. Technol., vol. 56, no. 4, pp. 2124–2133, 2022.

S. Kabir, R. U. Islam, M. S. Hossain, and K. Andersson, “An integrated approach of Belief Rule Base and Convolutional Neural Network to monitor air quality in Shanghai,” Expert Syst. Appl., vol. 206, p. 117905, 2022.

T. Williams, R. Shum, and D. Rappleye, “Concentration Measurements In Molten Chloride Salts Using Electrochemical Methods,” J. Electrochem. Soc., vol. 168, no. 12, p. 123510, 2021.

S. Ginting, “Analysis of the Mediation Role of Career Adaptability In the Relationship Between Emotional Intelligence and Achievement Motivation For Generation Z Students in West Kalimantan,” Aptisi Trans. Technopreneursh., vol. 4, no. 3, pp. 325–335, 2022.

J. Chung and H.J. Kim, “An automobile environment detection system based on deep neural network and its implementation using IoT-enabled in-vehicle air quality sensors,” Sustainability, vol. 12, no. 6, p. 2475, 2020.

Z. Kedah, “Use of e-commerce in the world of business,” Startupreneur Bus. Digit. (SABDA Journal), vol. 2, no. 1, pp. 51–60, 2023.

S. Sonawani and K. Patil, “Air quality measurement, prediction and warning using transfer learning based IOT system for ambient assisted living,” Int. J. Pervasive Comput. Commun., no. ahead-of-print, 2023.

M. R. Nauvaldi, “Mobile Internet Analysis in Prevention of Negative Impacts of Information and Communication Technology in Indonesia,” IAIC Trans. Sustain. Digit. Innov., vol. 4, no. 2, pp. 137–145, 2023.

M. Li, S. Gao, F. Lu, H. Tong, and H. Zhang, “Dynamic estimation of individual exposure levels to air pollution using trajectories reconstructed from mobile phone data,” Int. J. Environ. Res. Public Health, vol. 16, no. 22, p. 4522, 2019.

Q. Zhang, F. Fu, and R. Tian, “A deep learning and image-based model for air quality estimation,” Sci. Total Environ., vol. 724, p. 138178, 2020.

N. Mungoli, “Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networks,” arXiv Prepr. arXiv2304.02653, 2023.

P. Lestari, M. K. Arrohman, S. Damayanti, and Z. Klimont, “Emissions and spatial distribution of air pollutants from anthropogenic sources in Jakarta,” Atmos. Pollut. Res., vol. 13, no. 9, p. 101521, 2022.