ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume VIII-4/W1-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W1-2021, 89–96, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W1-2021-89-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W1-2021, 89–96, 2021
https://doi.org/10.5194/isprs-annals-VIII-4-W1-2021-89-2021

  03 Sep 2021

03 Sep 2021

DESIGN AND RESULTS OF AN AI-BASED FORECASTING OF AIR POLLUTANTS FOR SMART CITIES

L. Petry1, T. Meiers2, D. Reuschenberg2, S. Mirzavand Borujeni2, J. Arndt2, L. Odenthal2, T. Erbertseder3, H. Taubenböck3, I. Müller3, E. Kalusche3, B. Weber4, J. Käflein5, C. Mayer6, G. Meinel1, C. Gengenbach7, and H. Herold1 L. Petry et al.
  • 1Leibniz Institute of Ecological Urban and Regional Development (IOER), Dresden, Germany
  • 2Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (HHI), Berlin, Germany
  • 3German Remote Sensing Data Center, German Aerospace Center (DLR), Weßling-Oberpfaffenhofen, Germany
  • 4Hof University of Applied Sciences (HSH), Hof, Germany
  • 5Geomer GmbH, Heidelberg, Germany
  • 6Meggsimum, Mutterstadt, Germany
  • 7Software AG, Darmstadt, Germany

Keywords: Air Pollutants, Forecasting, Artificial Intelligence, RNN, Smart City, Co-Design

Abstract. This paper presents the design and the results of a novel approach to predict air pollutants in urban environments. The objective is to create an artificial intelligence (AI)-based system to support planning actors in taking effective and adequate short-term measures against unfavourable air quality situations. In general, air quality in European cities has improved over the past decades. Nevertheless, reductions of the air pollutants particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3), in particular, are essential to ensure the quality of life and a healthy life in cities. To forecast these air pollutants for the next 48 hours, a sequence-to-sequence encoder-decoder model with a recurrent neural network (RNN) was implemented. The model was trained with historic in situ air pollutant measurements, traffic and meteorological data. An evaluation of the prediction results against historical data shows high accordance with in situ measurements and implicate the system’s applicability and its great potential for high quality forecasts of air pollutants in urban environments by including real time weather forecast data.