An Efficient Filter Based Weighted Prior and Posterior Clustering Framework for Air Pollution Prediction - Ch. Lakshmi Kumari, V. Phani Krishna, Nallamala Sri Hari, Atmakuri Krishna Chaitanya, K. V. Prasad, Ravi Aavula (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The problem of aggregating similar air quality indicators due to sample variability arises when the number of datasets detailing air pollution continues to grow. In order to estimate comparable severity levels out of scant and ambiguous data, traditional techniques of air quality clustering rely on static markers. Locating both local and global data samples for the purpose of severity categorization is an additional problem involved with the single cluster metric-based clustering approach that is currently being used. For the purpose of data clustering, this study presents a hybrid outlier identification model. The goal of this model is to determine which air quality characteristic values are the most severe. We recommend use a clustering technique that considers both local and global measures in order to classify the data that has been filtered. In the end, it all boils down to utilizing the categorization learning model in order to make a prediction regarding how poor the air quality would be. An outlier-based data clustering and classification framework has been shown to perform better than the conventional methods for predicting air quality, according to the results of an experiment conducted on a time series air pollution dataset.