LSTM-Based Air Quality Prediction

Abstract

In this project we are using Neural networks and Long Short-Term Memory (LSTM) to address the air pollution detection. As each living organism needs fresh and good quality air for every moment, very few of the living things can survive without such air. Increasing industry and populace have end up fundamental contributor for the air pollutants. Over the time, many countries are finding numerous approaches of fighting towards air pollution. The air we breathe every moment causes several health hazards.  So we'd like an honest system that predicts such pollutions and is useful in better environment. It leads us to address the advance techniques for predicting the pollution using   Air Quality Index. So, here we are predicting air pollution using LSTM and Neural Network techniques for the coming hour mainly on pollutants like ammonia (NH3), lead (Pb), ozone(O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and ecosystem   aspects inclusive of temperature, strain, rainfall, wind pace according to minute and wind direction.