Spatiotemporal Anomaly-Aware Air Quality Forecasting In South Korea Using Multi-Channel Attention-Based Deep Learning
Keywords:
: Air Quality Forecasting, Graph Attention Networks, Anomaly-Aware Learning, Spatiotemporal Prediction, LSTM.Abstract
Air quality is becoming a global issue in present days and the monitoring of the air quality is also becoming an important subject for prediction and awareness about air pollutants. In this study an investigation of air quality forecasting has been done with the help of deep learning methods as isolation forest and autoencoders. Data has been collected as sequential data from Korean government meteorological websites from 2018 to 2022 and a spatiotemporal anomaly-aware forecasting is done with graphical attention network combined with LSTM in the encoder part. The study is an integration of spatial correlations among multiple stations of South Korea and the temporal trend and prediction of the pollutants and handling the missing data or outliers in the pollutant reading. Moreover, the incorporation of novel anomaly-aware loss penalizes the outliers more cautiously leads to a stable reading. Experimental results and prediction plots confirm that the proposed model achieves more stable and accurate forecasts. This research highlights the effectiveness of graph-based learning and anomaly-aware strategies in environmental time-series prediction tasks.
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