Air Quality Prediction usingKNN and LSTM

Abstract

The project titled "Air Quality Prediction Using KNN and LSTM" endeavors toaddress the critical issue of air pollutionthrough the application of advanced computational techniques. The project aimsto develop a robust predictive model that can forecast air quality levels based on historical data, meteorological parameters, and relevant environmental features. Leveraging machine learning algorithms such as regression, decision trees, or neural networks, the project seeks to analyze complex relationships within the data and enhance the accuracy of air qualitypredictions. The methodology involves the collection and preprocessing of extensive datasets encompassing pollutant concentrations, weather conditions, and geographicalinformation. The selected machine learning algorithms will be trained on this data to recognize patterns and correlations, enabling the model to make accurate predictions. The project also explores the integration of real- time data streams, satellite imagery, and sensor networks to improve the responsiveness of the predictive model.