基于SSA-LSTM模型的空气质量预测研究

Research on Air Quality Prediction Based on SSA-LSTM Model

为提高PM_(2.5)浓度的预测精度,提出了一种结合麻雀搜索算法(SSA)和长短期记忆神经网络(LSTM)的组合预测模型。以2023年5月至8月期间长沙市PM_(2.5)浓度数据为基础,构建了SSA-LSTM模型并与其他模型进行了对比实验。实验结果显示,SSA-LSTM模型的预测结果在拟合优度(R^(2))上相较于单一LSTM、PSO-LSTM和WOA-LSTM模型分别提升了45.93%、31.55%、19.12%,同样在均方根误差(RMSE)和平均绝对误差(MAE)的结果上也表现更优,表明该模型在PM_(2.5)浓度预测方面具有高准确性和有效性,可为制定PM_(2.5)相关预防措施提供一定的参考价值。

To improve the accuracy of PM_(2.5)concentration prediction,a combined prediction model integrating Sparrow Search Algorithm(SSA)and Long Short-Term Memory(LSTM)neural networks is proposed.The SSA-LSTM model is developed based on PM_(2.5)concentration data from Changsha city,spanning from May to August in 2023,and is compared with other models.The results show that the SSA-LSTM model significantly outperformed the standalone LSTM,PSO-LSTM,and WOA-LSTM models in terms of fit quality(R^(2)),registering improvements of 45.93%,31.55%,and 19.12%,respectively.Similarly,it also shows superior performance in terms of Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).These findings demonstrate the model has high accuracy and effectiveness in PM_(2.5)concentration prediction,providing a certain reference value for making the PM_(2.5)-related preventive measures.