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Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification

机译：基于遗传算法优化反向传播神经网络和支持向量机数据分类的臭氧浓度预测方法

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Multi Artificial Neural Network (ANN) models are used to forecast ozone concentration on single-site for a better forecast accuracy in huge dataset condition. Support Vector Machine (SVM) is used to accurately classify the data into its corresponding categories. Back Propagation neural network (BPNN) was optimized using Genetic Algorithm (CA) to achieve higher forecast stability. SVM classification and GA optimized BPNN (GABPNN) were combined to forecast ozone concentrations in Beijing. The ozone measurements of XiDan sampling site in Beijing were used to test the effectiveness of this method. The modeling dataset used were the records of temperature (T), humidity (H), wind velocity (WV) and UV radiation (UV) from Mar 2009 to Jul 2009. The models were tested using the records of Aug 2009. High accuracy was achieved using this forecast method. Correlation coefficient (R) of the final models on the test stage ranged from 0.86 to 0.90, with an average of 0.87. The predictions of the final models represented a great forecasting capability that could be applied to the real-life ozone forecast in Beijing.
机译：多人工神经网络（ANN）模型用于在单个站点上预测臭氧浓度，从而在庞大的数据集条件下具有更好的预测精度。支持向量机（SVM）用于将数据准确分类为相应的类别。使用遗传算法（CA）对反向传播神经网络（BPNN）进行了优化，以实现更高的预测稳定性。支持向量机分类和遗传算法优化的BPNN（GABPNN）相结合来预测北京的臭氧浓度。用北京西单采样点的臭氧测量结果验证了该方法的有效性。使用的建模数据集是2009年3月至2009年7月的温度（T），湿度（H），风速（WV）和UV辐射（UV）的记录。使用2009年8月的记录对模型进行了测试。使用这种预测方法可以实现。最终模型在测试阶段的相关系数（R）在0.86至0.90的范围内，平均为0.87。最终模型的预测代表了强大的预测能力，可以将其应用于北京的实际臭氧预测。

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