TY - JOUR ID - 21538 TI - A hybrid model for online prediction of PM2:5 concentration: A case study JO - Scientia Iranica JA - SCI LA - en SN - 1026-3098 AU - Sadabadi, Y. S. AU - Salari, M. AU - Esmaili, R. AD - Department of Industrial Engineering, Ferdowsi University of Mashhad, Mashhad, Iran AD - Environmental Pollution Monitoring Center of Mashhad, Deputy of Services, and Urban Environment, Municipality of Mashhad, Iran Y1 - 2021 PY - 2021 VL - 28 IS - 3 SP - 1699 EP - 1710 KW - PM2.5 KW - Prediction KW - neural networks KW - Wavelet transformation KW - Monte Carlo simulation KW - WRF model DO - 10.24200/sci.2019.5120.1107 N2 - In this paper, we aim at developing a model to predict the daily average concentration of particulate matters with a diameter of less than 2.5 micrometers (PM2.5). In the introduced model, we incorporate Weather Research and Forecasting (WRF) meteorological model, Monte Carlo simulation, wavelet transform, and multilayer perceptron (MLP) neural networks. In particular, the MLP and wavelet transformation are combined for prediction. In order to predict the model’s input parameters, including wind speed, wind direction, temperature, rainfall, and temperature inversion, the WRF meteorological model is used. Finally, according to the available uncertainty in the input data and in order to achieve a more accurate prediction, the Monte Carlo simulation is utilized. In order to assess the effectiveness of the model in the real world, it has been conducted in an online mode for 35 days. Numerical results give an acceptable accuracy in terms of some widely used measures. In particular, taking into account the R measurements, it is equal to 0.831 over the set of test instances. UR - https://scientiairanica.sharif.edu/article_21538.html L1 - https://scientiairanica.sharif.edu/article_21538_c65efd89bd3fe7fc8ceb9b9cf639599a.pdf ER -