Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

Owing to climate change, industrial pollution, and population gathering, the air quality status in many places China is not optimal. The continuous deterioration of air-quality conditions has considerably affected economic development health China’s people. However, diversity complexity factors which affect pollution render monitoring data complex nonlinear. To improve accuracy prediction index (AQI) obtain more accurate AQI with respect their nonlinear nonsmooth characteristics, this study introduces an model based on empirical mode decomposition (EMD) LSTM uses improved particle swarm optimization (IPSO) identify optimal parameters. First, performed EMD obtained uncoupled intrinsic function (IMF) components after removing noisy data. Second, we built EMD–IPSO–LSTM for each IMF component extracted values. Third, results validation analyses algorithm showed that compared EMD–LSTM, had higher fitting effect, provided theoretical technical support management pollution.