Prediction of PM2.5 concentration based on the CEEMDAN-RLMD-BiLSTM-LEC model

ABSTRACT: Air quality has emerged as a critical concern in recent years, with the concentration of PM2.5 recognized as a vital index for assessing it. The accuracy of predicting PM2.5 concentrations holds significant value for effective air quality monitoring and management. In response to this, a combined model comprising CEEMDAN-RLMD-BiLSTM-LEC has been introduced, analyzed, and compared against various other models. The combined decomposition method effectively underlines the fundamental characteristics of the data compared to individual decomposition techniques. Additionally, local error correction (LEC) efficiently addresses the issue of prediction errors induced by excessive disturbances. The empirical results of nine steps indicate that the combined CEEMDAN-RLMD-BiLSTM-LEC model outperforms s

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PROVIDER: S-EPMC10470446 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature