A novel approach for forecasting PM2.5 pollution in Delhi using CATALYST.

Air pollution is one of the main environmental issues in densely populated urban areas like Delhi. Predictions of the PM2.5 concentration must be accurate for pollution reduction strategies and policy actions to succeed. This research article presents a novel approach for forecasting PM2.5 pollution in Delhi by combining a pre-trained CNN model with a transformer-based model called CATALYST (Convolutional and Transformer model for Air Quality Forecasting). This proposed strategy uses a mixture of the two models. To derive attributes of the PM2.5 timeline of data, a pre-existing CNN model is utilized to transform the data into visual representations, which are analyzed subsequently. The CATALYST model is trained to predict future PM2.5 pollution levels using a sliding window training approach on extracted features. The model is utilized for analyzing temporal dependencies in PM2.5 time-series data. This model incorporates the advancements in the transformer-based architecture initially designed for natural language processing applications. CATALYST combines positional encoding with the Transformer architecture to capture intricate patterns and variations resulting from diverse meteorological, geographical, and anthropogenic factors. In addition, an innovative approach is suggested for building input-output couples, intending to address the problem of missing or partial data in environmental time-series datasets while ensuring that all training data blocks are comprehensive. On a PM2.5 dataset, we analyze the proposed CATALYST model and compare its performance with other standard time-series forecasting approaches, such as ARIMA and LSTM. The outcomes of the experiments demonstrate that the suggested model works better than conventional methods and is a potential strategy for accurately forecasting PM2.5 pollution. The applicability of CATALYST to real-world scenarios can be tested by running more experiments on real-world datasets. This can help develop efficient pollution mitigation measures, impacting public health and environmental sustainability.
(© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)