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näkemys - Air Quality Forecasting
Accurate Real-Time Forecasting of Regional Air Pollutant Concentrations using a Novel Wavelet-Based CatBoost Model
A novel WaveCatBoost architecture that combines the maximal overlapping discrete wavelet transform (MODWT) with the CatBoost model to generate accurate and robust real-time forecasts of air pollutant concentrations.
Leveraging Future Covariates for Accurate NO2 Forecasting using Spatiotemporal Graph Neural Networks
This paper presents a novel forecasting methodology that leverages both past and future covariates, such as weather forecasts and calendar events, to accurately predict NO2 concentrations using Spatiotemporal Graph Neural Networks (STGNNs).
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