Air pollution prediction by using an artificial neural network model.
Air pollution prediction by using an artificial neural network model.
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DOI：
10.1007/s10098-019-01709-w
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发表时间：
2019-08
影响因子：
4.3
通讯作者：
Rahmati M
中科院分区：
文献类型：
--
作者：
Maleki H;Sorooshian A;Goudarzi G;Baboli Z;Birgani YT;Rahmati M
空气污染物影响公共卫生、社会经济、政治、农业和环境。本研究的目的是评估人工神经网络 (ANN) 算法预测伊朗阿瓦士一整年（2009 年 8 月至 2010 年 8 月）每小时标准空气污染物浓度和两个空气质量指数（空气质量指数 (AQI) 和空气质量健康指数 (AQHI)）的能力。众所周知，阿瓦士是世界上污染最严重的城市之一，这主要是由于沙尘暴造成的。应用的算法涉及输入阶段的 9 个因素（5 个气象参数、提前 3 和 6 小时的污染物浓度、时间和日期），隐藏阶段的 30 个神经元，最后一级的输出。当比较使用 5% 和 10% 数据进行验证和测试的性能时，在这两个阶段使用 5% 数据的结果更可靠。对于四个地点检查的所有六种标准污染物（O3、NO2、PM10、PM2.5、SO2 和 CO），比较预测和测量时的相关系数 (R) 和均方根误差 (RMSE) 值分别为 0.87 和 59.9。当比较建模和测量的 AQI 和 AQHI 时，R2 通过 AQHI 对三个地点显着，而 AQI 仅在一个地点显着。这项研究表明，人工神经网络适用于阿瓦士等城市预测空气质量，以防止健康影响。我们的结论是，城市空气质量当局、从业者和决策者可以应用人工神经网络来估计污染物和空气质量指数的时空分布。建议进一步研究将人工神经网络的效率和效力与数值、计算和统计模型进行比较，以使管理者能够选择合适的工具包，以便在城市空气质量领域做出更好的决策。
Air pollutants impact public health, socioeconomics, politics, agriculture, and the environment. The objective of this study was to evaluate the ability of an artificial neural network (ANN) algorithm to predict hourly criteria air pollutant concentrations and two air quality indices, air quality index (AQI) and air quality health index (AQHI), for Ahvaz, Iran, over one full year (August 2009–August 2010). Ahvaz is known to be one of the most polluted cities in the world, mainly owing to dust storms. The applied algorithm involved nine factors in the input stage (five meteorological parameters, pollutant concentrations 3 and 6 h in advance, time, and date), 30 neurons in the hidden phase, and finally one output in last level. When comparing performance between using 5% and 10% of data for validation and testing, the more reliable results were from using 5% of data for these two stages. For all six criteria pollutants examined (O3, NO2, PM10, PM2.5, SO2, and CO) across four sites, the correlation coefficient (R) and root-mean square error (RMSE) values when comparing predictions and measurements were 0.87 and 59.9, respectively. When comparing modeled and measured AQI and AQHI, R2 was significant for three sites through AQHI, while AQI was significant only at one site. This study demonstrates that ANN has applicability to cities such as Ahvaz to forecast air quality with the purpose of preventing health effects. We conclude that authorities of urban air quality, practitioners, and decision makers can apply ANN to estimate spatial–temporal profile of pollutants and air quality indices. Further research is recommended to compare the efficiency and potency of ANN with numerical, computational, and statistical models to enable managers to select an appropriate toolkit for better decision making in field of urban air quality.
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