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Corani, G. (2005). Air Quality Prediction in Milan: Feed-Forward Neural Networks, Pruned Neural Networks and Lazy Learning. Ecological Modelling, 185, 513-529. https://doi.org/10.1016/j.ecolmodel.2005.01.008

has been cited by the following article:

  • TITLE: Analysis of Tropospheric Ozone by Artificial Neural Network Approach in Beijing

    AUTHORS: Muhammad Azher Hassan, Zhaomin Dong

    KEYWORDS: Neural Network, Surface Ozone, Air Pollution, NN Modeling, Ozone in Beijing

    JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.6 No.11, November 16, 2018

    ABSTRACT: Higher concentration of tropospheric ozone in atmosphere reveals its adverse effects on human health, plants, and on environment. So, there is a need for atmospheric pollutants analysis and their concentration variation, which is a key factor for air quality management in urban areas. The Beijing Olympic center site was used as area of study and five recorded meteorological parameters temperature, dew point, wind speed, pressure, and relative humidity were employed as inputs imputes. Nitrogen Dioxide (NO2) and hour of day are also considered as input parameters for modeling of tropospheric ozone concentrations. Several deterministic methods are available for local air quality forecasting and prediction. But, in this study, multilayer perceptron (MLP) and generalized regression neural model (GRNM) were considered for prediction of ozone ground level concentration. The root mean squared errors (RMSE) and mean absolute error (MAE) value for MLP model were lower, which confirms its fitness for forecasting purpose. Regression coefficient for MLP in this study was calculated 0.91 and for GRNM model provides 0.76 value. The dew point and relative humidity were the most dominant input imputes found by model, which results in higher concentration of tropospheric ozone.