Volume 2 Number 4 Year - 2020
Number of articles: 13
AIR QUALITY INDEX FORECASTING USING HYBRID NEURAL NETWORK MODEL WITH LSTM ON AQI SEQUENCES
Abstract: This paper presents an approach to forecasting air pollution levels measured as Air Quality Index (AQI) metric using hybrid Long Short-Term Memory (LSTM) models. The pollution levels have been found to vary in a particular pattern that depends on both the overall climate or season as well as the hour of the day. The hybrid model captures these 2 patterns and makes the prediction of AQI of some future hour. It employs 2 separate LSTM models that are trained on time-series data of AQI gathered at different time lags i.e. hourly and daily. The final output is given as a weighted sum of the 2 outputs produced by LSTM model. Upon comparing the performance of the standalone hour-wise forecasting LSTM model and the hybrid model it was found the latter gives the minimum error metric given an appropriate weight is chosen.
Keywords: LSTM; Hybrid model; AQI, forecast; Time series
DOI: 10.24874/PES02.04.010   Downloads: 33