An integrated neural network model for PM10 forecasting
An integrated neural network model for PM10 forecasting
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DOI：
10.1016/j.atmosenv.2006.01.010
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发表时间：
2006-05-01
影响因子：
5
通讯作者：
Reyes, J
中科院分区：
文献类型：
--
作者：
Perez, P;Reyes, J
我们已经开发了一个集成的人工神经网络模型来预测的最大值的24小时平均的PM 10浓度提前一天，我们已经应用到的情况下，在智利的圣地亚哥市的5个监测站。该模型的输入数据是当天五个监测站在下午7点之前测得的浓度，加上气象变量的测量值和预测值。输出为相同五个监测站第二天的预期最大浓度。五个预测中浓度最大的一个定义了第二天的空气质量。根据浓度下降的范围，空气质量分为三个级别：好（A）、差（B）和临界（C）。我们调整了模型的参数，使用2001年和2002年的数据预测2003年的条件和2002年和2003年的数据，以预测2004年的值。使用神经模型的预测值进行了比较与线性模型具有相同的输入变量和持久性所获得的结果。根据这里报告的结果，总的来说，神经模型似乎更准确，尽管输入变量的良好选择似乎非常重要。(c)2006爱思唯尔有限公司保留所有权利。
We have developed an integrated artificial neural network model to forecast the maxima of 24h average of PM 10 concentrations I day in advance and we have applied it to the case of five monitoring stations in the city of Santiago, Chile. Inputs to the model are concentrations measured until 7 PM at the five stations on the present day plus measured and forecast values of meteorological variables. Outputs are the expected maxima concentrations for the following day at the site of the same five stations. The greatest of the concentrations among the five forecasts defines air quality for the following day. According to the range where the concentrations fall, three levels or classes of air quality are defined: good (A), bad (B) and critical (C). We have adjusted the parameters of the models using 2001 and 2002 data to forecast 2003 conditions and 2002 and 2003 data in order to forecast 2004 values. Forecast values using the neural model are compared with the results obtained with a linear model with the same input variables and with persistence. According to the results reported here, overall, the neural model seems more accurate, although a good choice of input variables appears to be very important. (c) 2006 Elsevier Ltd. All rights reserved.