Design, development, and evaluation of ML-IoT enabled environment quality monitoring system

Abstract

With the worsening environment quality credited to rapid urbanization and industrialization, ensuring optimal indoor air quality is a critical challenge with far-reaching implications for public health, environmental sustainability, and overall wellbeing. Despite its importance, real-time air quality monitoring solutions (AQMS) remain underdeveloped, leaving significant gaps in the ability to analyze and predict environment quality effectively. In this paper, we have presented the development of a Machine Learning Internet of Things (ML-IoT) enabled AQMS equipped with diverse sensors capable of sensing vital environmental parameters which include carbon monoxide, carbon dioxide, particulate matter PM2.5, PM10, temperature, and humidity. We deployed the developed AQMS in an indoor environment and collected environmental data from sensors. A comprehensive preprocessing pipeline, including outlier removal using the interquartile range method and feature scaling, was applied to improve data quality. Three machine learning models including Linear Regression (LR), Random Forest (RF), and Bidirectional Long Short-Term Memory (BiLSTM) were implemented for predictive analysis. Using state-of-the-art data analytics, we have tested these ML models to uncover trends, identify correlations, and predict air quality metrics with improved accuracy. To ensure methodological rigor, 5-fold cross-validation was applied to LR and RF models, while time-series cross-validation was used for BiLSTM to preserve temporal dependencies. The results show that LR achieves high accuracy for temperature prediction with R 2 of 96.45% and cross validation R 2 of 95.4%, while RF provides moderate performance for CO 2 prediction with R 2 of 52.7% and 64.05% in cross validation. BiLSTM improves CO 2 prediction with R 2 of 89.9%) under standard evaluation and achieves an average R 2 of 85.4% under time-series validation, demonstrating generalization. To provide real-time visualization of air quality parameters, we have designed a user-friendly dashboard, allowing stakeholders to monitor real-time conditions and derive actionable insights. The developed AQMS finds promising application in industries, houses, office and residential buildings, allowing predictive environment quality monitoring and triggering alarm in case of any anomaly.