IoT Based Air Pollution Monitoring and Data Analytics Using Machine Learning Approach
Keywords:
Arima, NodeMCU, MQ-series sensors, IoT, predictive modelling, and machine learningAbstract
A NodeMCU equipped with an ESP8266 WLAN connection and a MQ Series sensor are combined in an IoT-based air contaminant monitoring system to transmit sensor readings to the Ubidots cloud. Additional components of this study include a guaging model, which is simply a subset of prescient modelling, and a realistic AI model to predict the degree of air pollution. We will use our IoT device as a model to collect the data, and to extend our model, we used an authorised open-source dataset provided by the US Government. The paper's main objectives are to track, conceive of contaminated information, and determine it. To choose the optimal predictive model and a forecasting model for calculating the air quality index (AQI) of four different gases—Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Ozone—three machine learning (ML) calculations were specifically carried out (O3). Here, linear regression, arbitrary woods, and xgboost are used for ML computations, while an ARIMA model is used for time-series estimation. The exhibition measurements were based on Mean Outright Error and Root Mean Square Error (RMSE) (MAE).
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