中圖分類號： TP399
文獻標識碼： A
DOI：10.16157/j.issn.0258-7998.222731
中文引用格式： 韋詩玥，徐洪珍. 基于改進BCCSA和深層LSTM的空氣質量預測方法[J].電子技術應用，2022，48(6)：28-32.
英文引用格式： Wei Shiyue，Xu Hongzhen. Air quality prediction method based on improved BCCSA and deep LSTM[J]. Application of Electronic Technique，2022，48(6)：28-32.

Air quality prediction method based on improved BCCSA and deep LSTM

Abstract： The existing air quality prediction methods rarely consider seasonal factors, and the prediction effect is not good. Therefore, an air quality prediction method based on improved binary chaotic crow search algorithm(BCCSA) and deep long short term memory neural network(LSTM) is proposed. Firstly, the method of seasonal adjustment is proposed to preprocess the collected original air quality data in order to eliminate the influence of season on prediction. Then, an improved BCCSA is proposed to optimize the air quality data. Finally, the self-attention mechanism is added to the deep LSTM to predict the air quality data. The experimental results show that this method can effectively improve the prediction accuracy of air quality.

Key words : air quality；seasonal adjustment；improved binary chaotic crow search algorithm(BCCSA)；deep long short term memory(LSTM)；self-attention mechanism