Research on a Novel Hybrid Decomposition-Ensemble Learning Paradigm Based on VMD and IWOA for PM2.5 Forecasting

A new hybrid optimization prediction model for PM2.5 concentration considering other air pollutants and meteorological conditions

With the development of economy, the problem of air pollution has become increasingly serious. As an important detection index of air pollutants, how to accurately and effectively predict PM2.5 concentration is a significant issue related to human health and development. In this paper, a new hybrid optimization prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition optimized by COOT optimization algorithm (COOT-VMD), and least square support vector machine (LSSVM) optimized by the JAYA optimization algorithm (JAYA-LSSVM), named CEEMDAN-COOT-VMD-JAYA-LSSVM, is proposed. To avoid artificially setting the limits of the decomposition layer and penalty factor of VMD parameters, an improved VMD by COOT optimization algorithm, named COOT-VMD, is proposed. First, the original sequence of PM2.5 concentration is decomposed by CEEMDAN. Second, the high complexity component with low prediction accuracy after once decomposition is decomposed by COOT-VMD. Third, a prediction model of optimized LSSVM by JAYA optimization algorithm, named JAYA-LSSVM, is proposed. JAYA-LSSVM is used to predict all components considering other air pollutants such as PM10, SO2, NO2, CO, and O3 and meteorological conditions such as wind speed, temperature, sunlight, relative humidity and average air pressure. Finally, all predicted values are reconstructed to obtain the final prediction results. PM2.5 concentration from April 1, 2016 to March 29, 2021 in Xi'an and Shenyang is used as the experimental data to verify the proposed model. The results of experiment in Xi'an show that the RMSE, MAE, MAPE and R² are 2.843, 1.8344, 2.94%, and 0.99525 respectively. The results of experiment in Shenyang show that the RMSE, MAE, MAPE and R² are 2.2714, 1.673, 3.13%, and 0.99573 respectively. Compared to other single and hybrid models, the proposed model can accurately predict PM2.5 concentration. Diebold Mariano test results display the proposed prediction model is superior to all comparison models at 99% confidence level.

Regional VOCs Gathering Situation Intelligent Sensing Method Based on Spatial-Temporal Feature Selection

As VOCs pose a threat to human health, it is important to accurately capture changes in VOCs concentrations and sense VOCs concentrations in relevant areas. Therefore, it is necessary to improve the accuracy of VOCs concentration prediction and realise the VOCs aggregation situation sensing. Firstly, on the basis of regional grid division, the inverse distance spatial interpolation method is used for spatial interpolation to collect regional VOCs data information. Secondly, extreme gradient boosting (XGBoost) is used for spatio-temporal feature selection, combined with graph convolutional neural network (GCN) to construct regional spatial relationships of VOCs, and multiple linear regression (MLR) to process VOCs time series data and predict the VOCs concentration in the grid. Finally, the aggregation potential values of VOCs are calculated based on the prediction results, and the potential perception results are visualised. A VOCs aggregation perception method based on concentration prediction is proposed, using the XGBoost-GCN-MLR method with a scenario-aware approach for VOCs to perceive the VOCs aggregation in the relevant region. VOCs concentration prediction and VOCs aggregation trend perception were carried out in Xi’an, Baoji, Tongchuan, Weinan and Xianyang. The results show that compared with the GCN model, XGBoost model, MLR model and GCN-MLR model, the XGBoost-GCN-MLR model reduces the input variables, achieves the optimisation of the input parameters of the VOCs concentration prediction model, reduces the complexity of the prediction model and improves the prediction accuracy. Intelligent sensing of VOCs aggregation can visualise the regional VOCs. The intelligent sensing of VOCs aggregation can visualise the development trend and status of regional VOCs aggregation and convey more information, which has practical value.