Tariq, HasanTouati, FaridCrescini, DamianoMnaouer, Adel Ben2023-09-122023-09-122023-03Tariq, H., Touati, F., Crescini, D., & Mnaouer, A. B. (2023). IoT-Based Bi-Cluster Forecasting Using Automated ML-Model Optimization for COVID-19. Atmosphere, 14(3), 534. https://doi.org/10.3390/atmos1403053420734433https://doi.org/10.3390/atmos14030534https://hdl.handle.net/20.500.12519/821The current COVID-19 pandemic has raised huge concerns about outdoor air quality due to the expected lung deterioration. These concerns include the challenges associated with an increase of harmful gases like carbon dioxide, the iterative/repetitive inhalation due to mask usage, and harsh environmental temperatures. Even in the presence of air quality sensing devices, these challenges can hinder the prevention and treatment of respiratory diseases, epidemics, and pandemics in severe cases. In this research, a dual time series with a bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and responses that use an automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e., a multi-variate dual time-series sensor, of the environmental and US Environmental Protection Agency standard, which measures variables for the air quality index using air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor the air quality and their data was collected at Ubidots IoT platform over GSM. The results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 s for the control and an RMSE of 1.646 in a training time of 28.53 s when optimized. The estimated R-Squared error was 0.03, with the Mean-Square Error for temperature being 1.0084 °C, and 293.98 ppm for CO2. Furthermore, the Mean-Absolute Error (MAE) for temperature was 0.66226 °C and 10.252 ppm for the correlated-CO2 at a predicted speed of ~5100 observations/s. In the sample cluster for temperature, 45,000 observations/s for CO2 was achieved due to the iterative optimization of the training time (469.28 s). The correlated temperature and a time of 28.53 s for CO2 were very promising in forecasting COVID-19 countermeasures before time. © 2023 by the authors.enThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).COVID-19environmental mappingforecastingindoor air qualityIoTmachine learningpandemicArticleCopyright : © 2023 by the authors.