AIR QUALITY INDEX FORECASTING USING HYBRID NEURAL NETWORK MODEL WITH LSTM ON AQI SEQUENCESJournal: Proceedings on Engineering Sciences (Vol.2, No. 4)
Publication Date: 2020-12-31
Authors : Shirshendu Roy Pratyay Mukherjee;
Page : 431-440
Keywords : LSTM; Hybrid model; AQI; forecast; Time series;
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.
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