基于KNN-LSTM的PM2.5浓度预测模型

目前多数PM_2.5浓度预测模型仅利用单个站点的时间序列数据进行浓度预测, 并没有考虑到空气质量监测站之间的区域关联性, 这会导致预测存在一定的片面性. 本文利用KNN算法选择目标站点所在区域中与其相关的空间因素, 并结合LSTM模型, 提出基于时空特征的KNN-LSTM的PM_2.5浓度预测模型. 以哈尔滨市10个空气质量监测站的污染物数据进行仿真实验, 并将KNN-LSTM模型与其他预测模型进行对比, 结果显示: 模型相较于BP神经网络模型平均绝对误差(MAE)、均方根误差(RMSE)分别降低了19.25%、13.23%; 相较于LSTM模型MAE、RMSE分别降低了4.29%、6.99%. 表明本文所提KNN-LSTM模型能有效提高LSTM模型的预测精度.

PM2.5 Concentration Prediction Model Based on KNN-LSTM

At present, most PM_2.5 concentration prediction models only use time series data from a single station for concentration prediction, but do not take into account the regional correlation among air quality monitoring stations. This will lead to a certain one-sidedness of the prediction. In this paper, the KNN algorithm was used to select the relevant spatial factors in the area where the target site is located. Combined with the LSTM model, a KNN-LSTM PM_2.5 concentration prediction model based on spatiotemporal features was proposed. The simulation experiments were performed on pollutant data from 10 air quality monitoring stations in Harbin, and the KNN-LSTM model was also compared with other prediction models. The results show that the model compared with the BP neural network model, Mean Absolute Error (MAE), Mean Square Root Error (RMSE) decrease by 19.25% and 13.23% respectively; compared with the LSTM model, MAE and RMSE decreased by 4.29% and 6.99% respectively. It shows that the KNN-LSTM model proposed in this study can effectively improve the prediction accuracy of LSTM model.