VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting
This work addresses air quality prediction for public health in Delhi, but it is incremental as it builds on existing LSTM methods with adaptive attention.
The paper tackles air pollution forecasting in Delhi by predicting pollutant concentrations for the next 24 hours using historical and real-time data, achieving a 15-20% accuracy improvement over offline models and outperforming baselines by 3-5%.
Air pollution is the leading environmental health hazard globally due to various sources which include factory emissions, car exhaust and cooking stoves. As a precautionary measure, air pollution forecast serves as the basis for taking effective pollution control measures, and accurate air pollution forecasting has become an important task. In this paper, we forecast fine-grained ambient air quality information for 5 prominent locations in Delhi based on the historical and real-time ambient air quality and meteorological data reported by Central Pollution Control board. We present VayuAnukulani system, a novel end-to-end solution to predict air quality for next 24 hours by estimating the concentration and level of different air pollutants including nitrogen dioxide ($NO_2$), particulate matter ($PM_{2.5}$ and $PM_{10}$) for Delhi. Extensive experiments on data sources obtained in Delhi demonstrate that the proposed adaptive attention based Bidirectional LSTM Network outperforms several baselines for classification and regression models. The accuracy of the proposed adaptive system is $\sim 15 - 20\%$ better than the same offline trained model. We compare the proposed methodology on several competing baselines, and show that the network outperforms conventional methods by $\sim 3 - 5 \%$.