基于VMD与组合模型的大气污染物浓度预测方法  

Air Pollution Concentration Prediction Method Based on VMD and Combination Model

作　　者：邵玉祥[1] 冯春生 程俊杰 刘秋梦 蒲思涵 SHAO Yuxiang;FENG Chunsheng;CHENG Junjie;LIU Qiumeng;PU Sihan(School of Computer Science,China University of Geosciences,Wuhan 430074,China)

机构地区：[1]中国地质大学(武汉)计算机学院,湖北武汉430074

出　　处：《软件导刊》2024年第4期8-13,共6页Software Guide

基　　金：国家自然科学基金项目(U1711267)。

摘　　要：为提高大气污染物浓度的预测准确性,提出一种基于变分模态分解与组合模型的预测方法。首先通过变分模态分解将目标监测点的历史污染物浓度数据重构为多变量时序数据,根据区域内监测点之间的地理关系构建时空序列数据;然后将处理好的数据输入LSTM与ConvLSTM的组合模型中,同时提取时间与空间特征并输出预测结果。针对武汉市PM2.5、SO2、NO23种污染物历史浓度数据进行实验,所提预测方法在MAE、RMSE和MAPE3个指标上均表现最佳,明显优于其他模型。此外,在时间尺度增加的情况下,该方法相较其他模型仍保持最高的预测精度。该方法能够充分捕捉局部特征,在综合考虑时间与空间特征方面具备显著优势,为大气污染物浓度的准确预测提供了一种可行途径。To improve the accuracy of predicting atmospheric pollutant concentrations,a prediction method based on variational mode decom-position and combination model is proposed.First,the variational mode decomposition reconstructs the historical pollutant concentration data of the target monitoring point into multivariate temporal data,constructs spatiotemporal sequence data based on the geographical relationships be-tween monitoring points in the region;Second,input the processed data into a combination model of LSTM and ConvLSTM to extract both tem-poral and spatial features and output prediction results.Based on the historical concentration data of PM2.5,SO2,and NO2 pollutants in Wuhan City,the proposed prediction method performed the best in MAE,RMSE,and MAPE indicators,significantly outperforming other models.In addition,as the time scale increases,this method still maintains the highest prediction accuracy compared to other models.This method can fully capture local features and has significant advantages in considering both temporal and spatial features,providing a feasible approach for accurate prediction of atmospheric pollutant concentrations.

关 键 词：大气污染物 浓度预测 变分模态分解 组合模型 LSTM ConvLSTM 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]