工业大气污染物浓度的复合自回归网络预测

Forecasting Method for Industrial Exhaust Gas Based on Compound Nonlinear Auto Regressive Neural Network

针对工业园区大气污染管理中预测能力较弱的问题,考虑工业大气污染物的多因素耦合及非线性时序特征,提出一种工业大气污染物浓度预测方法。根据预测指标数值特征,提出复合自回归神经网络(CNAR)。对目标预测指标及影响因素进行关联分析及时序建模,实现对工业大气污染物浓度的短期预测。选用河北省某市大气网格化监测数据进行模型训练与方法验证,实验结果表明CNAR预测模型可对工业大气污染物浓度进行有效预测,效果优于传统自回归神经网络,为工业大气污染防控提供参考依据。

For the weak prediction ability of the exhaust gas in the industrial park management,a prediction method of industrial exhaust gas concentration is proposed considering the multiple factors coupling and its nonlinear timing characteristics.A Compound Nonlinear Auto Regressive(CNAR)neural network is proposed based on the numerical characteristics of the predictive indices.The relationship between target prediction index and related influencing factors are used to model the correlation and temporal relationship.Then the short-term prediction of exhaust gas concentration is realized.The atmospheric grid monitoring data of a city in Hebei province is used to train model and verify method,and the experimental results show that the CNAR forecasting model can predict the gas concentration effectively in a short time.The prediction accuracy is higher than the traditional auto regressive neural network,and this method provides a reference basis for the exhaust gas control in industrial park.