Resumen de Forecast Daily Air-Pollution Time Series with Deep Learning
Air-quality in urban areas is one of the most critical concern for governs. Wide spectrum measures are implemented in relation to this issue, from laws and promotion of renewal of heating and transport systems, to stablish monitoring and prediction systems. When air-pollutant levels excess from healthy thresholds, traffic limitations are activated with non-negligible nuisances, and social and economic impacts. For this reason, high-pollution episodes must be appropriately anticipated. In this work, deep learning-based implementations are evaluated for forecasting daily values of three pollutants: CO, NO2, and O3, at three types of monitoring station from the air-quality time series provided by Madrid City Council. In this analysis, the influence of working-non-working days and the use of multivariant input, composed of multiple-pollutants time series, is also evaluated. As a consequence, a rank of the most suitable algorithms for forecasting air-quality time series is stated.
