A Hybrid Deep Learning Model for Multi-step Ahead Prediction of PM2.5 Concentration Across India

Fine particulate matter (PM2.5) concentration in ambient air has become a major concern across the globe. All major cities of India have reported an elevated concentration of PM2.5 that has severe consequences to the health, economy, and ecosystem of the region. As a result, it becomes imperative to develop adequate tools for forecasting particulate matter concentration. Most of the research works mostly focused on single-step prediction horizon, thereby limiting their use. In the present work, a hybrid model has been proposed to forecast multi-step ahead concentrations of PM2.5 in ambient air across India covering different agroclimatic zones. The hybrid model architecture was an encoder-decoder-based sequence to sequence model framework that was built with convolutional long short-term memory (LSTM), bidirectional LSTM and 3D convolution neural network. The model was tested across 26 Indian cities covering 13 major agroclimatic zones of India. The performance of the model was also analysed for consecutive hour sequential prediction taking last 24-h data as input to the model. The model output was also compared with signal to noise ratio to explore the reason for variations in model performance. A distinct trend was found between signal to noise ratio and model output. As noise increases, the model performances suffer. Overall, the model was found to be stable as its performance errors across different time horizon has little variations. The proposed model has the potential to be used for long-term forecasting by incorporating other predictor variables series.