School of Mathematical Sciences, Shanxi University, Taiyuan, China.
School of Mathematical Sciences, Shanxi University, Taiyuan, China.
Chemosphere. 2022 Dec;308(Pt 1):136180. doi: 10.1016/j.chemosphere.2022.136180. Epub 2022 Sep 1.
Accurate predicting the air quality trend can provide a theoretical basis for environmental protection management and decision-making. This study proposed the convolutional neural networks-long short-term memory (CNN-LSTM) model, which was proposed to improve the air quality prediction accuracy. Firstly, CNN's efficient feature extraction function was used to extract data features. Then the feature vectors were constructed into the sequence form, which was transmitted to the LSTM network. The LSTM layer learned the changing rules of air quality data to predict future data. Taking Beijing's air quality index as an example, the prediction results of the CNN-LSTM model were compared with those of auto-regressive moving average (ARMA), seasonal auto-regression integrated moving average (SARIMA), recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) models. The results show that, compared with other single prediction models, the CNN-LSTM achieved the highest prediction accuracy. In particular, CNN-LSTM was compared with the SARIMA model, which is a time series representative model. The indicators of the CNN-LSTM model have been well improved. The mean absolute error (MAE) and root mean square error (RMSE) of the CNN-LSTM were reduced respectively 3.17% and 5.46%, and R was improved 8.45%.
准确预测空气质量趋势可为环境保护管理和决策提供理论依据。本研究提出了卷积神经网络-长短期记忆（CNN-LSTM）模型，旨在提高空气质量预测精度。首先，利用 CNN 的高效特征提取功能提取数据特征。然后将特征向量构建成序列形式，并将其传输到 LSTM 网络。LSTM 层学习空气质量数据的变化规律，以预测未来数据。以北京空气质量指数为例，将 CNN-LSTM 模型的预测结果与自回归移动平均（ARMA）、季节性自回归综合移动平均（SARIMA）、递归神经网络（RNN）、长短期记忆（LSTM）和门控循环单元（GRU）模型的预测结果进行比较。结果表明，与其他单一预测模型相比，CNN-LSTM 实现了最高的预测精度。特别是，CNN-LSTM 与 SARIMA 模型进行了比较，SARIMA 模型是时间序列的代表性模型。CNN-LSTM 模型的指标得到了很好的改进。CNN-LSTM 的平均绝对误差（MAE）和均方根误差（RMSE）分别降低了 3.17%和 5.46%，R 提高了 8.45%。
