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- W4320165138 abstract "Currently, air pollution is a severe problem, because pollutants such as Particulate matter of 2.5 micrometers affect human health. Therefore, several works address the prediction of this pollutant, using statistical methods and machine learning. However, these predictions are performed in places of a city, where air quality monitoring stations are available, which is not always possible due to their high implementation and maintenance costs. Thus, in this work, we propose an architecture based on a Conditional Generative Adversary Network to create new synthetic data and interpolate this pollutant in places where monitoring stations are missing." @default.
- W4320165138 created "2023-02-13" @default.
- W4320165138 creator A5022490401 @default.
- W4320165138 date "2019-12-08" @default.
- W4320165138 modified "2023-09-27" @default.
- W4320165138 title "Interpolation and Prediction of PM2.5 based on Conditional Generative Adversarial Network and a forecasting model" @default.
- W4320165138 doi "https://doi.org/10.52591/lxai2019120828" @default.
- W4320165138 hasPublicationYear "2019" @default.
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