PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network

• URL: http://arxiv.org/abs/2006.09204v1
• Date: Sun, 14 Jun 2020 22:50:51 GMT
• Title: PlumeNet: Large-Scale Air Quality Forecasting Using A Convolutional LSTM Network
• Authors: Antoine All\'eon, Gr\'egoire Jauvion, Boris Quennehen, David Lissmyr
• Abstract summary: This paper presents an engine able to forecast jointly the concentrations of the main pollutants harming people's health. The forecasts are performed on a regular grid with a neural network whose architecture includes convolutional LSTM blocks. A valuable advantage of the engine is that it does not need much computing power: the forecasts can be built in a few minutes on a standard GPU.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an engine able to forecast jointly the concentrations of the main pollutants harming people's health: nitrogen dioxyde (NO2), ozone (O3) and particulate matter (PM2.5 and PM10, which are respectively the particles whose diameters are below 2.5 um and 10 um respectively). The forecasts are performed on a regular grid (the results presented in the paper are produced with a 0.5{\deg} resolution grid over Europe and the United States) with a neural network whose architecture includes convolutional LSTM blocks. The engine is fed with the most recent air quality monitoring stations measures available, weather forecasts as well as air quality physical and chemical model (AQPCM) outputs. The engine can be used to produce air quality forecasts with long time horizons, and the experiments presented in this paper show that the 4 days forecasts beat very significantly simple benchmarks. A valuable advantage of the engine is that it does not need much computing power: the forecasts can be built in a few minutes on a standard GPU. Thus, they can be updated very frequently, as soon as new air quality measures are available (generally every hour), which is not the case of AQPCMs traditionally used for air quality forecasting. The engine described in this paper relies on the same principles as a prediction engine deployed and used by Plume Labs in several products aiming at providing air quality data to individuals and businesses.

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