AIR MAP- Deep Learning Prediction in Air Quality for Smarter Decisions

Abstract

Air Map is a forward-thinking web application which provides customers with up-to date information about air quality and weather forecasting. Pollutant levels and weather are predicted via deep learning model using historic data extracted carefully from sensors. This dataset undergoes data pre-processed to ensure its accuracy and trained by deep learning model on this vast data to predict the future parameters with high accuracy. These trends can be better perceived by using visualization tools such as time-series plot and heat maps for better understanding for customers. This application can be customized to receive timely notification on percentage of hazardous gases and total Air Quality index up to time. This application act as a catalyst for environmental awareness for both public and government, by this forecasting citizens will understand the air quality and weather patterns and for government this visualization will help to understand the weather and air quality patterns to mitigate air pollution by applying norms. This application works towards a healthier and more sustainable future. Key Words: Air Quality Index, Sensor Data, Deep Learning Models, LSTM, Statistical analysis and metrices, Air Map integration