Predicting and Visualizing Air Quality Index (AQI) using IoT and Machine Learning: An Experimental Study
Keywords:
Air quality index, Deep learning, Environmental sensing, IoT, LSTM, Machine learning, Meteorological parameters, PM2.5, Real-time monitoring, ThingSpeakAbstract
Air quality deterioration has emerged as a critical environmental and public health challenge globally, particularly in rapidly urbanizing regions. This research presents a comprehensive framework for real-time Air Quality Index (AQI) prediction using Internet of Things (IoT) technology integrated with advanced machine learning algorithms. We developed a Portable AQI Detector Kit equipped with multi-pollutant sensors and deployed it in Indore, Madhya Pradesh, India, to collect continuous data on PM2.5, PM10, NOx, SO2, and CO concentrations. An improved long short-term memory (ILSTM) neural network model was developed and compared against traditional forecasting methods, including LSTM, ARIMA, and SARIMA. The ILSTM model achieved superior performance with an RMSE of 39.688 and an R² of 0.973, outperforming other baseline models. Additionally, we developed a Multivariate Recursive Linear Regressive Model (MRLRM) that incorporates meteorological parameters (temperature, humidity, wind speed) to enhance prediction accuracy, achieving an R² improvement of 0.068 over Support Vector Regression models. The system employs the MQTT protocol for cloud-based data transmission to the ThingSpeak platform, enabling real-time visualization and public accessibility. This integrated approach demonstrates significant advancement in predictive environmental monitoring, providing actionable insights for pollution control and public health protection.
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