MACHINE LEARNING–BASED PREDICTION OF AIR QUALITY LEVELS ACROSS INDIAN CITIES USING ENVIRONMENTAL MONITORING DATA
DOI:
https://doi.org/10.69980/zjd1mp66Keywords:
Air Quality Index, machine learning, air pollution forecasting, Indian cities, environmental monitoringAbstract
Air pollution is a key environmental and public health problem in rapidly urbanizing Indian cities. The present study aimed to design and assess the performance of machine learning models for the prediction of the next day’s air-quality categories, based on the environmental monitoring data available at large-scale. The data set includes 235,785 observations from 291 cities in 32 states and union territories in India for the period of April 2022 to April 2025. Following the calculation of lagged AQI values and moving-average indicators, 219,888 AQI model ready observations were retained. The predictors included temporal and geographical variables, pollutants, and monitoring variables, and the train-test split was a chronological split to avoid information leakage. Models evaluated were multinomial logistic regression, decision tree, random forest and histogram gradient boosting models with respect to performance metrics including accuracy, balanced accuracy, precision, recall, macro F1 score, weighted F1 score and confusion matrices. The highest overall accuracy was obtained by histogram gradient boosting, while the highest macro F1 score was obtained by the random forest algorithm and the performance was most balanced across the six AQI categories. Random forest model gave the best prediction for Good, Satisfactory and Moderate conditions, but reduced prediction accuracy for Very Poor and Severe conditions due to significant class imbalance. The majority of errors in the AQI range were between neighbouring categories. The results prove that machine learning is very useful for the air-quality forecasting in multiple cities, early-warning systems and evidence-based environmental management in India.
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