Time Series On-Board Air Quality Index
Abstract views: 166
,
695 downloads: 174
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.
Downloads
References
[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 Androidâ€, 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 Fuzzyâ€, 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)â€, 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.
Copyright (c) 2023 Benedictus Augusta Vianney, Bayu Erfianto
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Manuscript submitted to IndoJC has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals.Â
- Copyright on any article is retained by the author(s). Regarding copyright transfers please see below.
- Authors grant IndoJC a license to publish the article and identify itself as the original publisher.
- Authors grant IndoJC commercial rights to produce hardcopy volumes of the journal for sale to libraries and individuals.
- Authors grant any third party the right to use the article freely as long as its original authors and citation details are identified.
- The article and any associated published material is distributed under the Creative Commons Attribution 4.0License
