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Indoor Air Pollution Forecasting Using Deep Neural Networks

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13264)


Atmospheric pollution components have negative effects in the health and life of people. Outdoor pollution has been extensively studied, but a large portion of people stay indoors. Our research focuses on indoor pollution forecasting using deep learning techniques coupled with the large processing capabilities of the cloud computing. This paper also shares the implementation using an open source approach of the code for modeling time-series of different sources data. We believe that further research can leverage the outcomes of our research.


  • Air quality index forecast
  • Deep learning
  • Public data

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  • DOI: 10.1007/978-3-031-07750-0_12
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We thank the Asociación Mexicana de Cultura A.C., for its support. We also thank Montserrat Altamirano-Astorga for her valuable help proofreading this manuscript.

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Correspondence to Jorge Altamirano-Astorga , Ita-Andehui Santiago-Castillejos , Luz Hernández-Martínez or Edgar Roman-Rangel .

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Altamirano-Astorga, J., Santiago-Castillejos, IA., Hernández-Martínez, L., Roman-Rangel, E. (2022). Indoor Air Pollution Forecasting Using Deep Neural Networks. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham.

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