A multivariable sensor-agnostic framework for spatio-temporal air quality forecasting based on Deep Learning

Resumen

Recently, air quality has become a major concern for the protection of the environment and the well-being of people. Air pollution is a key proxy of the quality of life in any city and is crucial in the fight against climate change. Therefore, it is of utmost importance to analyze and forecast the concentration of pollutants. Current efforts in these tasks tend to focus on individual gases and rely on a static view of the measuring sensors. This work presents an Artificial Intelligence framework that forecasts the concentrations of eleven pollutants in a Region Of Interest for several forecast horizons. The framework is based on Convolutional Long Short-Term Memory networks. Unlike the reviewed state-of-the-art, this framework can work with multiple pollutants simultaneously. Furthermore, it can incorporate exogenous input data by means of independent modules. The framework can also deal with newly installed sensors and recover from missing data. An extensive 10-year hourly dataset was used to train and validate the framework. The aim of this work is to provide an operational air quality framework for real-world application. This implies not only modeling future air quality but also supporting sensor failures and adaptation to different pollutants. None of the reviewed related works covers all these characteristics to the best of the authors’ knowledge. In addition, they are incorporated while scoring error metrics similar to or better than the baselines. Specifically, normalized Root-Mean-Square Error improves 14.1, 0.8, and 6.8 compared to the persistence, three-dimensional convolutional, and Long Short-Term Memory models, respectively.