Enhancing Accuracy in Urban Air Quality Prediction: A Comparative Study of Predictive Algorithms for Air Pollutant Concentrations
Keywords:
Air Pollution, Air Quality, Algorithms, Comparative Analysis, India, Pollutants, Prediction models, RajasthanAbstract
Air pollution is a critical environmental issue affecting the health and well-being of populations globally. Thus, the accurate prediction of air pollution levels is essential for effective environmental management and public health interventions. Currently, research has focused on explaining the causes and temporal relationships among various factors affecting air quality, but very few studies have focused on the performance of wholesome forecasting models. This study conducted a comparative analysis of various algorithms employed to predict the concentration of diverse air pollutants in highly polluted cities in Rajasthan, India. Through extensive data collection and processing, this study evaluates the efficiency of eight different algorithms, namely, ARIMA, LR, SVM, Exponential Smoothing, Decision Tree Regressor, XG Boost, Random Forest and LSTM, for forecasting pollutant levels for the next five years, with the aim of identifying the most reliable and accurate models for air quality prediction in this specific geographical context. The findings suggest that the Decision Tree and XG Boost Provided the results with the highest accuracy of 42.39% and 42.38%, respectively. The findings also provide valuable insights for enhancing environmental monitoring and management strategies in regions facing severe air pollution challenges.
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