Affiliations: [a] School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Chennai, India | [b] Department of Computer Science, Pondicherry University, Pondicherry, India
Correspondence:
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Corresponding author: Siva Sathya S, Department of Computer Science, Pondicherry University, Pondicherry, India. E-mail: ssivasathya@pondiuni.ac.in.
Abstract: Air pollution is an alarming problem in many cities and countries around the globe. The ability to forecast air pollutant levels plays a crucial role in implementing necessary prevention measures to curb its effects in advance. There are many statistical, machine learning, and deep learning models available to predict air pollutant values, but only a limited number of models take into account the spatio-temporal factors that influence pollution. In this study a novel Deep Learning model that is augmented with Spatio-Temporal Co-Occurrence Patterns (STEEP) is proposed. The deep learning model uses the Closed Spatio-Temporal Co-Occurrence Pattern mining (C-STCOP) algorithm to extract non-redundant/closed patterns and the Diffusion Convolution Recurrent Neural Network (DCRNN) for time series prediction. By constructing a graph based on the co-occurrence patterns obtained from C-STCOP, the proposed model effectively addresses the spatio-temporal association among monitoring stations. Furthermore, the sequence-to-sequence encoder-decoder architecture captures the temporal dependencies within the time series data. The STEEP model is evaluated using the Delhi air pollutants dataset and shows an average improvement of 8%–13% in RMSE, MAE and MAPE metric compared to the baseline models.
Keywords: PM2.5 pollutant, spatio-temporal patterns, deep learning, time series prediction, delhi, air pollution
