Artificial neural network based vehicular pollution prediction model: a practical approach for urban air quality prediction
The achievement of local air quality management goals in densely populated built-up urban areas often requires short-term air quality plans or emission management policies. Vehicular pollution modelling is an effective tool in managing the vehicular exhaust emissions in urban environments. In the recent past, Artificial Neural Network (ANN) based vehicular pollution models are found to be useful alternatives to conventional deterministic and stochastic based models in predicting vehicular pollutants dispersion. This paper compares the prediction performance of the ANN-based short-term vehicular pollution model with some frequently used conventional models using real-time data at one of the densely populated built-up areas in the city of Delhi, India.
Keywords: urban air quality, air quality management, air pollution legislation, vehicle pollution, mathematical modelling, traffic emissions, artificial neural networks, ANNs pollution prediction, air quality prediction, pollutants dispersion, Delhi, India