Analyses and Predict the Air Pollution in India using Memory Based Learning Approaches

Abstract

Air pollution is a critical environmental issue affecting numerous countries around the world, and India is no exception. The alarming levels of air pollution in many Indian cities have serious implications for public health, the environment, and the overall quality of life. This abstract presents an analysis of the current state of air pollution in India and a prediction model to estimate future pollution levels. The analysis of air pollution in India involves examining various factors such as industrial emissions, vehicular pollution, biomass burning, and dust particles. Additionally, meteorological conditions, including temperature, wind speed, and rainfall, also play a significant role in air pollution levels. Data from monitoring stations, satellite imagery, and other relevant sources are used to gather information for the analysis. The study utilizes advanced data analytics techniques, including machine learning algorithms, to develop a predictive model for estimating air pollution levels. Historical pollution data, along with meteorological parameters, are used as inputs to train the model. The model's objective is to forecast pollution levels in different regions of India for specific timeframes, such as daily, weekly, or monthly intervals. The abstract concludes by discussing the potential applications of the analysis and prediction model. It highlights the significance of such models in aiding policymakers, urban planners, and environmental agencies in making informed decisions and implementing effective strategies to mitigate air pollution. The predictions can assist in issuing timely health advisories, implementing pollution control measures, and optimizing resource allocation for air quality management.