Time Series On-Board Air Quality Index

Authors

  • Benedictus Augusta Vianney Student
  • Bayu Erfianto

DOI:

https://doi.org/10.34818/INDOJC.2023.8.1.695

Keywords:

Air Quality Index, Fuzzy, ARIMA, LSTM

Abstract

With the rapid development of technology, individuals forget about their health, plus during the pandemic, the indoor air quality becomes more of a concern. Maintaining air quality to be healthy and good for humans is by keeping the amount of pollutants in the air, such as Carbon Dioxide (CO2), Volatile Organic Compound (VOC), and Formaldehyde (HCHO), at a predetermined and agreed threshold. We propose an on-board air quality index detection system for indoor and forecast the AQI in the future. The system will use a Raspberry Pi 4 and a WP6003 sensor device that will capture parameters for the AQI. The parameter data is analyzed using a correlation matrix to determine the parameters that affect each other. Then classified using fuzzy logic to determine the quality index based on the value of each parameter. Then forecast using the ARIMA and LSTM methods for the next 30 minutes. The forecasting accuracy is calculated using the RMSE and MAPE metrics. The result shows that CO2, VOC, and HCHO are related. Comparison of the forecasting results of the two methods concluded that the LSTM outperformed ARIMA to forecast the AQI for the next 30 minutes based on the previous 10 hours of data.

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References

[1] Mandal, T., Gorai, A.K. & Pathak, G. Development of fuzzy air quality index using soft computing approach. Environ Monit Assess 184, 6187–6196 (2012).
[2] MinJeong Kim, Richard D. Braatz, Jeong Tai Kim, ChangKyoo Yoo, Indoor air quality control for improving passenger health in subway platforms using an outdoor air quality dependent ventilation system. Building and Environment, Volume 92, Pages 407-417, 2015. Available: https://www.sciencedirect.com/science/article/pii/S036013231500222X
[3] Javid, Allahbakhsh et al. “Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI).” Iranian journal of public health vol. 45,2 (2016): 203-13.
[4] K. Pratama and E. Setiawan, “Implementasi Monitoring Kualitas Udara Menggunakan Peramalan Exponential Smoothing dan NodeMCU Berbasis Mobile Android”, Ultima Computing : Jurnal Sistem Komputer, vol. 9, no. 2, pp. 58-66, Apr. 2018.
[5] Hanna Febryna Simorangkir, “Rancang Bangun Pemantauan Kualitas Udara Pada Taman Wilayah Melalui Website Berbasis Arduino Menggunakan Logika Fuzzy”, Vol. 1 No. 1 (2107): JATI Vol. 1 No. 1, 2017.
[6] Zakaria, Nurul Azma et al. “Wireless Internet of Things-Based Air Quality Device for Smart Pollution Monitoring.” International Journal of Advanced Computer Science and Applications 9 (2018).
[7] Brainvendra Widi Dionova, M.N. Mohammed, S. Al-Zubaidi, Eddy Yusuf, Environment indoor air quality assessment using fuzzy inference system. ICT Express, Volume 6, Issue 3, 2020. Available:
https://www.sciencedirect.com/science/article/pii/S2405959520301065
[8] Rui Yan, Jiaqiang Liao, Jie Yang, Wei Sun, Mingyue Nong, Feipeng Li, Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering. Expert Systems with Applications, Volume 169, 2021. Available:
https://www.sciencedirect.com/science/article/pii/S095741742031157X
[9] Minqiu Zhou, Amir M. Abdulghani, Muhammad A. Imran, and Qammer H. Abbasi. 2020. Internet of Things (IoT) Enabled Smart Indoor Air Quality Monitoring System. In Proceedings of the 2020 International Conference on Computing, Networks and Internet of Things (CNIOT2020). Association for Computing Machinery, New York, NY, USA, 89–93.
[10] Bedekar, Gayatri and Patil, R.S. and Tergundi, Parimal and Goudar, R. H., An Efficient Implementation of ARIMA Technique for Air Quality Prediction, 2021. Available: https://ssrn.com/abstract=3889537
[11] Jaka Prayudha, Ardianto Pranata, and Afdal Al Hafiz, “Implementasi Metode Fuzzy Logic Untuk Sistem Pengukuran Kualitas Udara Di Kota Medan Berbasis Internet Of Things (IoT)”, Vol 4, No 2 (2018), 2018.
[12] T. Liu and S. You, “Analysis and Forecast of Beijing’s Air Quality Index Based on ARIMA Model and Neural Network Model,” Atmosphere, vol. 13, no. 4, p. 512, Mar. 2022.
[13] S. M. Saad, A. Y. M. Shakaff, A. R. M. Saad, A. M. Yusof, A. M. Andrew, A. Zakaria, and A. H. Adom, "Development of indoor environmental index: Air quality index and thermal comfort index", AIP Conference Proceedings 1808, 2017.
[14] R. Janarthanan, P. Partheeban, K. Somasundaram, P. Navin Elamparithi. A deep learning approach for prediction of air quality index in a metropolitan city. Sustainable Cities and Society, Volume 67, 2021. Available: https://doi.org/10.1016/j.scs.2021.102720
[15] J. Kang and K.-I. Hwang, “A Comprehensive Real-Time Indoor Air-Quality Level Indicator,” Sustainability, vol. 8, no. 9, p. 881, Sep. 2016, doi: 10.3390/su8090881.

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Published

2023-04-30

How to Cite

Benedictus Augusta Vianney, & Erfianto, B. (2023). Time Series On-Board Air Quality Index. Indonesian Journal on Computing (Indo-JC), 8(1), 23–36. https://doi.org/10.34818/INDOJC.2023.8.1.695

Issue

Vol. 8 No. 1 (2023): April, 2023

Section

Computer Science

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