Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation

Bayu Erfianto - Telkom University, Jl. Telekomunikasi No 1, Kabupaten Bandung, 40257, Indonesia
Andrian Rahmatsyah - Telkom University, Jl. Telekomunikasi No 1, Kabupaten Bandung, 40257, Indonesia

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Air quality monitoring is a process that determines the number of pollutants in the air, one of which is indoor air quality. The Fuzzy Indoor Air Quality Index was developed in this research. It is a method for determining the indoor air quality index using sensor fusion and fuzzy logic. By combining several different time series determinants of air quality, a fuzzy logic-based sensor fusion method is used to build a knowledge base about indoor air quality levels. Without the use of complicated calculation models, fuzzy logic-based fusion will make it easier to determine indoor air quality levels based on various sensor parameters. The input for fuzzy-based data fusion is obtained from the ARIMA method with Kalman Filter's air quality parameter values estimation. The application of ARIMA with a Kalman Filter was used to improve the accuracy of indoor air quality estimation in this study. ARIMA(3,1,3) had a MAPE of 0.1 percent on the CO2 dataset, and ARIMA(1,0,1) had a MAPE of 0.63 percent on the TVOC dataset based on approximately three experimental days. ARIMA (3,1,3) estimation with a Kalman Filter results in a MAPE of 0.03 percent for the CO2 dataset and a MAPE of 0.24 percent for ARIMA(1,0,1) Kalman Filter estimation on TVOC dataset. As a result, the Fuzzy Indoor Air Quality Index (FIAQI) developed in this research reasonably estimates indoor air quality. This can be seen by examining the percentage of estimation errors obtained from the experiment.


Sensor data fusion; fuzzy logic; air quality index; prediction; time series.

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J. Saini, M. Dutta, and G. Marques, “A comprehensive review on indoor air quality monitoring systems for enhanced public health,” Sustainable Environment Research, vol. 30, no. 1, 2020, doi: 10.1186/s42834-020-0047-y.

J. Ahn, D. Shin, K. Kim, and J. Yang, “Indoor air quality analysis using deep learning with sensor data,” Sensors (Switzerland), vol. 17, no. 11, Nov. 2017, doi: 10.3390/s17112476.

M. Hori, “Total Volatile Organic Compound (TVOC) as Index of Indoor Air Quality and Its Measuring and Evaluation,” Journal of the Human-Environment System, vol. 1, no. 1, pp. 1–11, 2020.

J. Fernández-Agüera, S. Dominguez-Amarillo, M. Fornaciari, and F. Orlandi, “TVOCs and PM 2.5 in naturally ventilated homes: Three case studies in a mild climate,” Sustainability (Switzerland), vol. 11, no. 22, Nov. 2019, doi: 10.3390/su11226225.

B. W. Dionova, M. N. Mohammed, S. Al-Zubaidi, and E. Yusuf, “Environment indoor air quality assessment using fuzzy inference system,” ICT Express, vol. 6, no. 3, pp. 185–194, Sep. 2020, doi: 10.1016/j.icte.2020.05.007.

J. Saini, M. Dutta, and G. Marques, Internet of Things for Indoor Air Quality Monitoring. Springer, 2021.

C. Guarnaccia, J. G. C. Breton, R. M. C. Breton, C. Tepedino, J. Quartieri, and N. E. Mastorakis, “ARIMA models application to air pollution data in Monterrey, Mexico,” in AIP Conference Proceedings, Jul. 2018, vol. 1982. doi: 10.1063/1.5045447.

Z. Ye, “Air Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method,” 2019. doi: 10.1051/e3sconf/20191360.

S. M. Abdullah, “To Predict Air Pollution using Machine Learning and Arima Model,” International Journal of Engineering Research & Technology (IJERT), vol. 10, no. 11, 2021.

Q. P. Ha, S. Metia, and M. D. Phung, “Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring,” IEEE Sensors Journal, vol. 20, no. 8, pp. 4430–4441, Apr. 2020, doi: 10.1109/JSEN.2020.2964396.

J. Saini, M. Dutta, and G. Marques, “Sensors for indoor air quality monitoring and assessment through Internet of Things: a systematic review,” Environmental Monitoring and Assessment, vol. 193, no. 2, Feb. 2021, doi: 10.1007/s10661-020-08781-6.

S. M. Saad et al., “Development of indoor environmental index: Air quality index and thermal comfort index,” AIP Conference Proceedings, vol. 1808, no. August, 2017, doi: 10.1063/1.4975276.

J. J. Carbajal-Hernández, L. P. Sánchez-Fernández, J. A. Carrasco-Ochoa, and J. F. Martínez-Trinidad, “Assessment and prediction of air quality using fuzzy logic and autoregressive models,” Atmospheric Environment, vol. 60, pp. 37–50, Dec. 2012, doi: 10.1016/j.atmosenv.2012.06.004.

Y. Wang, M. Zhao, Y. Han, and J. Zhou, “A fuzzy expression way for air quality index with more comprehensive information,” Sustainability (Switzerland), vol. 9, no. 1, 2017, doi: 10.3390/su9010083.

A. Javid, A. Abbas Hamedian, H. Gharibi, and M. H. Sowlat, “Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI),” Iran Journal of Public Health, vol. 45, no. 2, pp. 203–213, 2016.

G. Mani and R. Volety, “A comparative analysis of LSTM and ARIMA for enhanced real-time air pollutant levels forecasting using sensor fusion with ground station data,” Cogent Engineering, vol. 8, no. 1, 2021, doi: 10.1080/23311916.2021.1936886.

S. Ebrahim and F. Mofid, “Air Pollution Monitoring Using Fuzzy Logic in Industries,” Advanced Air Pollution, 2011, doi: 10.5772/16947.

S. Widodo, M. M. Amin, and A. Supani, “Design of Indoor Room Gas CO and SO2 Detection Based on Microcontroller Using Fuzzy Logic,” E3S Web of Conferences, vol. 125, 2019, doi: 10.1051/e3sconf/201912523013.

Y. Colella, A. S. Valente, L. Rossano, T. A. Trunfio, A. Fiorillo, and G. Improta, “A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection,” International Journal of Environmental Research and Public Health, vol. 19, no. 6, 2022, doi: 10.3390/ijerph19063533.

Sukarna, E. Y. P. Ananda, and M. S. Wahyuni, “Rainfall Forecasting Model Using ARIMA and Kalman Filter in Makassar, Indonesia,” in Journal of Physics: Conference Series, Dec. 2021, vol. 2123, no. 1. doi: 10.1088/1742-6596/2123/1/012044.

E. J. Bardana, “Indoor pollution and its impact on respiratory health,” Annals of Allergy, Asthma and Immunology, vol. 87, no. 6 SUPPL. 3, pp. 33–40, 2001, doi: 10.1016/S1081-1206(10)62338-1.


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