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Air Pollutants NO_2 Concentration Prediction Based on LSTM Neural Network method

机译：空气污染物NO_2基于LSTM神经网络方法的浓缩预测

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摘要

In recent years, the China's economy has developed rapidly. The people's living standard has been improved. The number of cars has been increasing, and the pollutant NO has been produced continuously, which leads to the formation of NO_2. These harmful particles have an impact on human health. Thus, the effective and accurate NO_2 concentration prediction model plays an effective role in people's health and prevention. For this problem, this paper presents a prediction model based on the long short-term memory (LSTM) method to predict NO_2 concentration. Firstly, the PM_(10), SO_2, NO_2, CO, O_3, temperature in a campus monitoring point in Beijing is collected as the research object in this paper. Then, the LSTM prediction model and BP (back propagation) neural network prediction model are established respectively. Finally, the accuracy of the two prediction models for the prediction of NO_2 concentration is compared. The results show that the prediction model based on LSTM method is superior to BP neural network model, and the prediction accuracy is more accurate.
机译：近年来，中国经济发展迅速。人民的生活水平得到了改善。汽车数量增加，污染物未持续生产，这导致NO_2的形成。这些有害颗粒对人体健康产生了影响。因此，有效和准确的NO_2浓度预测模型在人们的健康和预防中起着有效作用。对于这个问题，本文提出了一种基于长短期存储器（LSTM）方法来预测NO_2浓度的预测模型。首先，北京校园监测点中的PM_（10），SO_2，NO_2，CO，O_3温度作为本文的研究对象。然后，分别建立LSTM预测模型和BP（反向传播）神经网络预测模型。最后，比较了用于预测NO_2浓度的两种预测模型的准确性。结果表明，基于LSTM方法的预测模型优于BP神经网络模型，预测精度更准确。

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