Publication

Abstract : From the past few years due to the activities done by humans and industrialization the air pollution has become so dangerous in many countries especially in India as of the developing country. The main concern of people’s health is the particulate matter which is also known as PM 2.5 which is significant between the pollutant index. The particulate matter(PM) diameter is equal to or less than 2.5m is one of the major health issues when seen with all other air pollutants. The PM2.5 is one of those tiny particles which reduces one’s lucency and also the air becomes smoky when the elevation happens. In the urban areas, the PM2.5 hang on many factors,corresponding to the concentration on other pollutants and also on meteorology. To show up these factors there are some techniques which were introduced in some other air quality researches as well. These used approaches such as the neural network and Long Short-Term Memory (LSTM), to check every air pollutant level situated on traffic variables obtained and weather conditions. In our experiments, the results of our proposed method hybrid CNN-LSTM gives the most accurate prediction when compared to all other methods present and also performs a cut above than the guessing performance.