sis 21(33): e4

Research Article

Predicting the least air polluted path using the neural network approach

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  • @ARTICLE{10.4108/eai.29-6-2021.170250,
        author={K. Krishna Rani Samal and Korra Sathya Babu and Santos Kumar Das},
        title={Predicting the least air polluted path using the neural network approach},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        keywords={Air quality modelling, Routing, Deep learning, GIS, Kriging},
  • K. Krishna Rani Samal
    Korra Sathya Babu
    Santos Kumar Das
    Year: 2021
    Predicting the least air polluted path using the neural network approach
    DOI: 10.4108/eai.29-6-2021.170250
K. Krishna Rani Samal1,*, Korra Sathya Babu1, Santos Kumar Das1
  • 1: National Institute of Technology, Rourkela, India
*Contact email:


Air pollution exposure during daily transportation is becoming a critical issue worldwide due to its adverse effect on human health. Predicting the least air polluted healthier path is the best alternative way to mitigate personal air pollution exposure risk. Computing the least polluted path for the current time might not be helpful for real-time applications. Therefore, we develop a routing algorithm based on a neural network-based CNN-LSTM-EBK (CLE), a temporal-spatial interpolation model. The proposed model predicts pollution levels at high temporal granularity. This paper introduces a weight function to compute air pollution concentration at the road network. It also predicts the least air polluted path among all possible paths from a source to a destination at different time granularity. The results show that the predicted path may be longer than the shortest route but minimize pollution exposure risk all the time, which proves its effectiveness during daily transportation.