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Comparison of static MLP and dynamic NARX neural networks for forecasting of atmospheric PM10 and SO2 concentrations in an industrial site of Turkey
Environmental Forensics ( IF 1.8 ) Pub Date : 2020-06-01 , DOI: 10.1080/15275922.2020.1771637
Serdar Gündoğdu 1
Affiliation  

Abstract This study aims to compare performances of two static and one dynamic neural networks used for prediction of hourly ambient air quality concentrations in an industrial site of Turkey. Two air pollutants (PM10 and SO2) and three meteorological parameters (ambient air temperature, relative humidity, and wind speed) were used as input variables. The predictions of the dynamic nonlinear autoregressive exogenous (NARX) model were compared with the predictions of the static multilayer perceptron (MLP) neural network model. The results showed that the predictions of the NARX neural network were obviously better than the predictions of MLP networks. The coefficient of determination (R2), index of agreement and efficiency between the observed and predicted air pollutant concentrations by the NARX model were 0.9773, 0.994, and 0.977 for PM10, respectively while the same parameters were 0.9984, ≈1, and ≈1 for SO2. The MBEs (mean bias errors) were also approximately zero for both pollutants that indicate the adequacy of the model. The values of RMSE (root mean squared error) were also fractional as 0.0191 and 0.0087 for both pollutants. The NARX model predicted SO2 concentrations better than PM10 concentrations. In comparison with MLP network structures, NARX network exhibits faster convergence. The model suggested in this study could be used to support and improve air quality management practices.

中文翻译:

比较静态 MLP 和动态 NARX 神经网络,用于预测土耳其工业场所大气 PM10 和 SO2 浓度

摘要 本研究旨在比较两个静态和一个动态神经网络的性能,用于预测土耳其工业现场每小时环境空气质量浓度。两种空气污染物(PM10 和 SO2)和三个气象参数(环境气温、相对湿度和风速)被用作输入变量。动态非线性自回归外源 (NARX) 模型的预测与静态多层感知器 (MLP) 神经网络模型的预测进行了比较。结果表明,NARX神经网络的预测明显优于MLP网络的预测。NARX 模型观测到的和预测的空气污染物浓度之间的决定系数 (R2)、一致性指数和效率对于 PM10 分别为 0.9773、0.994 和 0.977,SO2 的相同参数分别为 0.9984、≈1 和 ≈1。两种污染物的 MBE(平均偏差误差)也近似为零,表明模型的充分性。对于两种污染物,RMSE(均方根误差)的值也为 0.0191 和 0.0087。NARX 模型预测 SO2 浓度优于 PM10 浓度。与 MLP 网络结构相比,NARX 网络表现出更快的收敛速度。本研究中建议的模型可用于支持和改进空气质量管理实践。NARX 模型预测 SO2 浓度优于 PM10 浓度。与 MLP 网络结构相比,NARX 网络表现出更快的收敛速度。本研究中建议的模型可用于支持和改进空气质量管理实践。NARX 模型预测 SO2 浓度优于 PM10 浓度。与 MLP 网络结构相比,NARX 网络表现出更快的收敛速度。本研究中建议的模型可用于支持和改进空气质量管理实践。
更新日期:2020-06-01
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