Performance of an AIOT-Particle Device for Air Quality and Environmental Data Prediction in Salatiga Area Using ARIMA Model

Johanes Dian Kurniawan, Suryasatriya Trihandaru, Hanna Arini Parhusip

Abstract


This study introduces the AIOT-Particle, a compact device designed for comprehensive air quality and environmental monitoring in Tegalrejo, Salatiga, Indonesia. Addressing the need for real-time, multi-parameter environmental data, the device simultaneously tracks PM1.0, PM2.5, temperature, humidity, pressure, and altitude, utilizing a built-in data fusion algorithm to ensure accurate and coherent data collection. Air pollution standards classify air quality as "good" (0–50), "moderate" (51–100), "unhealthy" (101-200), "very unhealthy" (201-300), and "hazardous" (>300). The research contribution is the development and validation of the AIOT-Particle using the ARIMA model for precise environmental monitoring. The methods involved deploying the device in Salatiga and applying the ARIMA model to analyze the collected data for accuracy. The results demonstrated promising accuracy: for PM1.0, the RMSE was 8.13 with an MAE of 6.04; for PM2.5, the RMSE was 6.60 with an MAE of 4.49. Environmental data analysis showed an RMSE of 0.74 for temperature (MAE 0.43), 2.11 for humidity (MAE 1.36), 0.25 for pressure (MAE 0.19), and 2.18 for altitude (MAE 1.70). These findings highlight the device's potential to enhance environmental surveillance and public health assessments, advance the understanding of air quality dynamics, and support targeted interventions to mitigate environmental risks. The novelty of this study lies in the integration of multiple environmental parameters into a single monitoring device, validated for accuracy using the ARIMA model.

Keywords


ARIMA; Air Quality; IoT; Prediction; Forecasting

Full Text:

PDF

References


M. Surdacki and M. Sobieszczanska, “The impact of particulate matter on prenatal and infant child development,” Alergol. Pol. - Polish J. Allergol., vol. 9, no. 3, pp. 179–185, 2022, doi: 10.5114/pja.2022.119230.

L. Zhang et al., “Indoor particulate matter in urban households: Sources, pathways, characteristics, health effects, and exposure mitigation,” Int. J. Environ. Res. Public Health, vol. 18, no. 21, 2021, doi: 10.3390/ijerph182111055.

B. Bessagnet et al., “Emissions of Carbonaceous Particulate Matter and Ultrafine Particles from Vehicles—A Scientific Review in a Cross-Cutting Context of Air Pollution and Climate Change,” Appl. Sci., vol. 12, no. 7, 2022, doi: 10.3390/app12073623.

P. Maciejczyk, L. C. Chen, and G. Thurston, “The role of fossil fuel combustion metals in PM2.5 air pollution health associations,” Atmosphere (Basel)., vol. 12, no. 9, pp. 1–34, 2021, doi: 10.3390/atmos12091086.

A. L. Moreno-Rios, L. P. Tejeda-Benitez, and C. F. Bustillo-Lecompte, “Sources, characteristics, toxicity, and control of ultrafine particles: An overview,” Geosci. Front., vol. 13, no. 1, 2022, doi: 10.1016/j.gsf.2021.101147.

F. de A. Lima, G. B. Medeiros, P. A. M. Chagas, M. L. Aguiar, and V. G. Guerra, “Aerosol Nanoparticle Control by Electrostatic Precipitation and Filtration Processes—A Review,” Powders, vol. 2, no. 2, pp. 259–298, 2023, doi: 10.3390/powders2020017.

T. F. Mebrahtu et al., “The effects of exposure to NO2, PM 25 and PM 10 on health service attendances with respiratory illnesses: A timeseries analysis,” Environ. Pollut., vol. 333, no. 2, p. 122123, 2023, doi: 10.1016/j.envpol.2023.122123.

S. Lim et al., “Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM2.5 exposure,” Sci. Total Environ., vol. 833, no. January, p. 155207, 2022, doi: 10.1016/j.scitotenv.2022.155207.

J. A. Saju, Q. H. Bari, K. A. B. M. Mohiuddin, and V. Strezov, “Measurement of ambient particulate matter (PM1.0, PM2.5 and PM10) in Khulna City of Bangladesh and their implications for human health,” Environ. Syst. Res., vol. 12, no. 1, 2023, doi: 10.1186/s40068-023-00327-2.

S. Khan and U. Khan, “Comparison of Forecasting Performance with VAR vs . ARIMA Models Using Economic Variables of Bangladesh,” vol. 10, no. 2, pp. 33–47, 2020, doi: 10.9734/AJPAS/2020/v10i230243.

D. Effrosynidis, E. Spiliotis, G. Sylaios, and A. Arampatzis, “Science of the Total Environment Time series and regression methods for univariate environmental forecasting : An empirical evaluation”,Sci. Total Environ., vol. 875, no. February,p. 162580, 2023, doi:10.1016/j.scitotenv.2023.162580

F. Wang and J. Aviles, “Contrasting Univariate and Multivariate Time Series Forecasting Methods for Sales: A Comparative Analysis,” Appl. Sci. Innov. Res., vol. 7, no. 2, p. p127, 2023, doi: 10.22158/asir.v7n2p127.

V. I. Kontopoulou, A. D. Panagopoulos, I. Kakkos, and G. K. Matsopoulos, “A Review of ARIMA vs. Machine Learning Approaches for Time Series Forecasting in Data Driven Networks,” Futur. Internet, vol. 15, no. 8, pp. 1–31, 2023, doi: 10.3390/fi15080255.

M. Wati, A. Haviluddin, A. Masyudi, H. Septiarini, and H. Rahmania, “Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 3, pp. 720–730, 2023, doi: 10.26555/jiteki.v9i3.22286.

A. L. Schaffer, T. A. Dobbins, and S. A. Pearson, “Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions,” BMC Med. Res. Methodol., vol. 21, no. 1, pp. 1–13, 2021, doi: 10.1186/s12874-021-01235-8.

J. Wang, Z. K. Pei, Y. Wang, and Z. Qin, “An investigation of income inequality through autoregressive integrated moving average and regression analysis,” Healthc. Anal., vol. 5, no. October 2023, p. 100287, 2024, doi: 10.1016/j.health.2023.100287.

J. Kaur, K. S. Parmar, and S. Singh, “Autoregressive models in environmental forecasting time series: a theoretical and application review,” Environ. Sci. Pollut. Res., vol. 30, no. 8, pp. 19617–19641, 2023, doi: 10.1007/s11356-023-25148-9.

M. Amjad, A. Khan, K. Fatima, O. Ajaz, S. Ali, and K. Main, “Analysis of Temperature Variability, Trends and Prediction in the Karachi Region of Pakistan Using ARIMA Models,” Atmosphere (Basel)., vol. 14, no. 1, p. 88, 2022, doi: 10.3390/atmos14010088.

F. R. Alharbi and D. Csala, “A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach,” Inventions, vol. 7, no. 4, 2022, doi: 10.3390/inventions7040094.

R. Ospina, J. A. M. Gondim, V. Leiva, and C. Castro, “An Overview of Forecast Analysis with ARIMA Models during the COVID-19 Pandemic: Methodology and Case Study in Brazil,” Mathematics, vol. 11, no. 14, pp. 1–18, 2023, doi: 10.3390/math11143069.

A. A. Ibrahim, B. N. Saeed, and M. A. Fadil, “Forecasting Stock Prices with an Integrated Approach Combining ARIMA and Machine Learning Techniques ARIMAML,” J. Comput. Commun., vol. 11, no. 08, pp. 58–70, 2023, doi: 10.4236/jcc.2023.118005.

O. M. Salah, G. J. M. Mahdi, and I. A. A. Al-Latif, “A modified ARIMA model for forecasting chemical sales in the USA,” J. Phys. Conf. Ser., vol. 1879, no. 3, 2021, doi: 10.1088/1742-6596/1879/3/032008.

A. S. Ahmar, M. Botto-Tobar, A. Rahman, and R. Hidayat, “Forecasting the Value of Oil and Gas Exports in Indonesia using ARIMA Box-Jenkins,” JINAV J. Inf. Vis., vol. 3, no. 1, pp. 35–42, 2022, doi: 10.35877/454ri.jinav260.

J. Wang, T. Ji, and M. Li, “A combined short-term forecast model of wind power based on empirical mode decomposition and augmented dickey-fuller test,” J. Phys. Conf. Ser., vol. 2022, no. 1, 2021, doi: 10.1088/1742-6596/2022/1/012017.

R. R. Barry and I. Bernarto, “Spurious Regression Analysis on Time Series Data From Factors Affecting Indonesian Human Development Indexs in 1990 – 2017,” JMBI UNSRAT (Jurnal Ilm. Manaj. Bisnis dan Inov. Univ. Sam Ratulangi)., vol. 7, no. 3, 2021, doi: 10.35794/jmbi.v7i3.30608.

Y. Al Moaiad et al., “The Internet of Things as A Revolution to Enhance the Technology of the Future,” Int. J. Spec. Educ., vol. 37, no. 3, pp. 2022–6521, 2022, doi: 10.13140/RG.2.2.19815.11686.

L. Stosic, M. Dimitrovska, L. Pushova Stamenkova, and M. Smelcerovic, “From Concept To Reality: Understanding the Internet of Things,” Sci. Int. J., vol. 2, no. 4, pp. 181–184, 2023, doi: 10.35120/sciencej0204181s.

K.Khan, “Internet of Things (IoT) in the Cloud : Connecting and Managing Smart Devices Amelia Mathias Department of Computer Science, University of Cambridge”, no. May, pp. 1-9, 2023.

S. A. Bkheet and J. I. Agbinya, “A Review of Identity Methods of Internet of Things (IOT),” Adv. Internet Things, vol. 11, no. 04, pp. 153–174, 2021, doi: 10.4236/ait.2021.114011.

I. Bagheri, “Big Data Analytics with a Vision Regarding Internet of Things ( IoT ),” no. December, 2023.

T. Gusman, M. Naeemullah, and A. M. Qasim, “Big Data Processing: A review,” Mesopotamian J. Big Data, pp. 23–30, 2022, doi: 10.58496/mjbd/2022/003.

M. E. E. Alahi et al., “Integration of IoT-Enabled Technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent Advancements and Future Trends,” Sensors, vol. 23, no. 11, 2023, doi: 10.3390/s23115206.

H. Allioui and Y. Mourdi, “Exploring the Full Potentials of IoT for Better Financial Growth and Stability: A Comprehensive Survey,” Sensors, vol. 23, no. 19, 2023, doi: 10.3390/s23198015.

T. Zachariah, N. Klugman, and P. Dutta, ThingSpeak in the Wild: Exploring 38K Visualizations of IoT Data, vol. 1, no. 1. Association for Computing Machinery, 2022. doi: 10.1145/3560905.3567766.

N. Sindhwani, R. Anand, R. Vashisth, S. Chauhan, V. Talukdar, and D. Dhabliya, “Thingspeak-Based Environmental Monitoring System Using IoT,” PDGC 2022 - 2022 7th Int. Conf. Parallel, Distrib. Grid Comput., no. April, pp. 675–680, 2022, doi: 10.1109/PDGC56933.2022.10053167.

B. HC, “Security exploration of MQTT protocol in Internet of Things,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 9, no. 3, pp. 3892–3897, 2020, doi: 10.30534/ijatcse/2020/209932020.

M. Bender, E. Kirdan, M. O. Pahl, and G. Carle, “Open-source MQTT evaluation,” 2021 IEEE 18th Annu. Consum. Commun. Netw. Conf. CCNC 2021, no. September, 2021, doi: 10.1109/CCNC49032.2021.9369499.

T. Christen, M. Hess, D. Grichnik, and J. Wincent, “Value-based pricing in digital platforms: A machine learning approach to signaling beyond core product attributes in cross-platform settings,” J. Bus. Res., vol. 152, no. August 2021, pp. 82–92, 2022, doi: 10.1016/j.jbusres.2022.07.042.

A. Jierula, S. Wang, T. M. Oh, and P. Wang, “Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data,” Appl. Sci., vol. 11, no. 5, pp. 1–21, 2021, doi: 10.3390/app11052314.

P. K. Jacobson, L. Lind, and H. L. Persson, “Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease,” Int. J. COPD, vol. 18, no. June, pp. 1457–1473, 2023, doi: 10.2147/COPD.S412692.

T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Geosci. Model Dev., vol. 15, no. 14, pp. 5481–5487, 2022, doi: 10.5194/gmd-15-5481-2022.

L.T. Kreutzer et al., “S-ACF : a selective estimator for the autocorrelation function of irregulary sampled time series 1 Introduction Basic Definitions”, vol. 5061, pp. 5049-5061, 2023.

G. Vishnu, D. Kaliyaperumal, P. B. Pati, A. Karthick, N. Subbanna, and A. Ghosh, “Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques,” World Electr. Veh. J., vol. 14, no. 9, pp. 1–17, 2023, doi: 10.3390/wevj14090266.

C. H. Weiss, B. Aleksandrov, M. Faymonville, and C. Jentsch, “Partial Autocorrelation Diagnostics for Count Time Series,” Entropy, vol. 25, no. 1, pp. 1–21, 2023, doi: 10.3390/e25010105.




DOI: http://dx.doi.org/10.26555/jiteki.v10i2.28490

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Johanes Dian Kurniawan, Hanna Arini Parhusip, Suryasatriya Trihandaru

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 
About the JournalJournal PoliciesAuthor Information
 

Jurnal Ilmiah Teknik Elektro Komputer dan Informatika
ISSN 2338-3070 (print) | 2338-3062 (online)
Organized by Electrical Engineering Department - Universitas Ahmad Dahlan
Published by Universitas Ahmad Dahlan
Website: http://journal.uad.ac.id/index.php/jiteki
Email 1: jiteki@ee.uad.ac.id
Email 2: alfianmaarif@ee.uad.ac.id
Office Address: Kantor Program Studi Teknik Elektro, Lantai 6 Sayap Barat, Kampus 4 UAD, Jl. Ringroad Selatan, Tamanan, Kec. Banguntapan, Bantul, Daerah Istimewa Yogyakarta 55191, Indonesia