Modelling of pollutants and particulate matter in air using auto-tuned deep recurrent networks

Abstract

Atmospheric pollutants and Particulate Matter of size less than 10µm (PM10) are becoming dominant in the atmosphere due to human activities and natural calamities. To address their associated problems on human health, the interactions between pollutants and PM10 have to be envisaged. Machine learning techniques like Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) were successfully employed in establishing the interactions between various factors at play. However, these techniques are denounced for following a heuristic approach for determining network hyper-parameters. We propose a novel evolutionary multi-objective optimization algorithm which can optimally determine the hyper-parameters in deep recurrent neural networks. We test the algorithm to build optimal RNNs and LSTMs for modelling and forecasting the pollutants and PM10 data generated in northern Taiwan region during the year 2015. A state-of-the-art network training algorithm, Truncated Back Propagation Through Time was used in our study and single variable regression was done for CO, NOx, SO2, and PM10. Except for SO2 with RNN, model developed with the proposed algorithm gave high R2 values. LSTM was found to be superior than RNN in all the cases with R2 going as high as 0.9584 for PM10, while that attained by RNN is 0.93.