Multi-source and multivariate ozone prediction based on fuzzy cognitive maps and evidential reasoning theory - Liu, Xiaoqian; Zhang, Yingjun; Wang, Jingping; Huang, Hua; Yin, Hui (Applied soft computing, 2022)
Ozone prediction, a key role for ozone pollution control, is facing the following challenges, i.e., the complex evolution trend of ozone, the cross-interference phenomena between ozone and other pollutants, and the low-quality monitoring data. To overcome the above challenges, we propose a multi-source and multivariate ozone prediction model based on fuzzy cognitive maps (FCMs) and evidential reasoning theory from the perspective of spatio-temporal fusion, termed as ERC-FCM. In this framework, an FCM-based prediction model is introduced to solve the ozone forecasting problem. Inspired by the multivariate time series forecasting, a multivariate ozone prediction problem is modeled as an FCM learned by the real-coded genetic algorithm, in which each node denotes a variable (pollutant). Thus, both the complex evolution trend of ozone and the cross-interference phenomena can be reflected by the FCM. Further, we propose an ensemble theoretical framework based on evidence reasoning theory and the matrix 2 norm. This theoretical framework relieves the negative factors from the low-quality monitoring data and improves the prediction accuracy when facing multi-source and multivariate time series. The performance of ERC-FCM is validated on two real-world datasets. The experimental results demonstrate that our method yields the best prediction performance by comparison with the other classical FCM-based methods on mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). In addition, the Friedman test and Nemenyi test show that ERC-FCM gets relatively better prediction accuracy than other models. •An ozone prediction model based on the fuzzy cognitive maps learning via the RCGA is proposed.•A matrix 2-norm weighting method is presented to objectively quantify the importance levels of different sources.•A multi-source and multivariate ozone prediction model based on FCMs and ER theory is proposed when facing low-quality time series from multiple stations.•Experimental results on two real-world data sets show that the proposed model achieves an overall performance improvement.