Vayuanukulani: Adaptive memory networks for air pollution forecasting

Madaan, D and Dua, Radhika and Mukherjee, P and et al, . (2019) Vayuanukulani: Adaptive memory networks for air pollution forecasting. In: 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP, 11-14 November 2019, Ottawa,Canada.

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Abstract

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 (NO2), particulate matter (PM2.5 and PM10) 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 ~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 ~7-18%.1

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IITH Creators:
IITH CreatorsORCiD
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Air quality, Deep Learning, Pollution forecasting, Real time air quality prediction, Indexed in Scopus
Subjects: Computer science
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 27 Feb 2020 08:05
Last Modified: 27 Feb 2020 08:05
URI: http://raiith.iith.ac.in/id/eprint/7483
Publisher URL: http://doi.org/10.1109/GlobalSIP45357.2019.8969343
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