Yazdanpanah H, Karimi M, Hejazizadeh Z
Department of Geography, Faculty of Humanities, University of Isfahan, Isfahan, Iran.
Environ Monit Assess. 2009 Oct;157(1-4):235-41. doi: 10.1007/s10661-008-0531-z. Epub 2008 Oct 9.
A neural network combined to an artificial neural network model is used to forecast daily total atmospheric ozone over Isfahan city in Iran. In this work, in order to forecast the total column ozone over Isfahan, we have examined several neural networks algorithms with different meteorological predictors based on the ozone-meteorological relationships with previous day's ozone value. The meteorological predictors consist of temperatures (dry and dew point) and geopotential heights at standard levels of 100, 50, 30, 20 and 10 hPa with their wind speed and direction. These data together with previous day total ozone forms the input matrix of the neural model that is based on the back propagation algorithm (BPA) structure. The output matrix is the daily total atmospheric ozone. The model was build based on daily data from 1997 to 2004 obtained from Isfahan ozonometric station data. After modeling these data we used 3 year (from 2001 to 2003) of daily total ozone for testing the accuracy of model. In this experiment, with the final neural network, the total ozone are fairly well predicted, with an Agreement Index 76%.
一个与人工神经网络模型相结合的神经网络被用于预测伊朗伊斯法罕市的每日大气臭氧总量。在这项工作中，为了预测伊斯法罕的总臭氧柱，我们基于臭氧与前一日臭氧值的气象关系，研究了几种使用不同气象预测因子的神经网络算法。气象预测因子包括温度（干球温度和露点温度）以及100、50、30、20和10百帕标准高度处的位势高度及其风速和风向。这些数据与前一日的总臭氧一起构成了基于反向传播算法（BPA）结构的神经网络模型的输入矩阵。输出矩阵是每日大气臭氧总量。该模型基于1997年至2004年从伊斯法罕臭氧测量站获得的每日数据构建。对这些数据进行建模后，我们使用了2001年至2003年这3年的每日总臭氧数据来测试模型的准确性。在这个实验中，使用最终的神经网络，总臭氧得到了较好的预测，一致性指数为76%。
