Zhongnan University of Economics and Law, Wuhan, 430073, China.
Sci Rep. 2023 Apr 5;13(1):5550. doi: 10.1038/s41598-023-32775-2.
The air quality index (AQI), as an indicator to describe the degree of air pollution and its impact on health, plays an important role in improving the quality of the atmospheric environment. Accurate prediction of the AQI can effectively serve people's lives, reduce pollution control costs and improve the quality of the environment. In this paper, we constructed a combined prediction model based on real hourly AQI data in Beijing. First, we used singular spectrum analysis (SSA) to decompose the AQI data into different sequences, such as trend, oscillation component and noise. Then, bidirectional long short-term memory (BiLSTM) was introduced to predict the decomposed AQI data, and a light gradient boosting machine (LightGBM) was used to integrate the predicted results. The experimental results show that the prediction effect of SSA-BiLSTM-LightGBM for the AQI data set is good on the test set. The root mean squared error (RMSE) reaches 0.6897, the mean absolute error (MAE) reaches 0.4718, the symmetric mean absolute percentage error (SMAPE) reaches 1.2712%, and the adjusted R reaches 0.9995.
空气质量指数（AQI）作为描述空气污染程度及其对健康影响的指标，在改善大气环境质量方面发挥着重要作用。准确预测 AQI 可以有效地为人们的生活服务，降低污染控制成本，提高环境质量。本文构建了一个基于北京市逐时 AQI 数据的组合预测模型。首先，我们使用奇异谱分析（SSA）将 AQI 数据分解为趋势、波动分量和噪声等不同序列。然后，引入双向长短期记忆网络（BiLSTM）对分解后的 AQI 数据进行预测，并使用轻梯度提升机（LightGBM）对预测结果进行集成。实验结果表明，SSA-BiLSTM-LightGBM 对测试集 AQI 数据集的预测效果良好，其均方根误差（RMSE）达到 0.6897，平均绝对误差（MAE）达到 0.4718，对称平均绝对百分比误差（SMAPE）达到 1.2712%，调整 R 达到 0.9995。
