Air pollutant concentration prediction based on a new hybrid model, feature selection, and secondary decomposition

AbstractThe concentration of air pollutants is closely related to people ’s production and life. Air quality prediction is the premise for environmental management departments to make decisions and put forward pollution control measures. A novel air pollutant prediction model was proposed in this paper to predict air pollutant concentration more accurately. Firstly, th e data were decomposed into several subsequences by a complete ensemble empirical mode decomposition with adaptive noise and calculated the sample entropy of the subsequence. Secondly, variational mode decomposition is used to decompose the sequence with the highest sample entropy, and a fast correl ation-based filter is used to select the features of the second decomposed sequence and the remaining sequences. Then, a multi-layer perceptron is used to predict the processed quadratic decomposition sequence, and a gated recurrent unit is used to predict the remaining sequences. According to the e xperimental results, three main conclusions can be drawn. First, through two groups of comparative experiments, it is found that the model has a good prediction effect. Second, after adding the decomposition algorithm, the average improvement levels of mean absolute error and root mean squared error were 44.50% and 34.77%, respectively. Third, after the re-decomposition of intrinsic mode functions 1, the mean absolute percentage error can be reduced by 22.98% on average on the original basis. The results of this study ...
Source: Air Quality, Atmosphere and Health - Category: Environmental Health Source Type: research