Research on Air Quality Prediction Based on SSA-LSTM Model
To improve the accuracy of PM2.5 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 PM2.5 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(R2),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 PM2.5 concentration prediction,providing a certain reference value for making the PM2.5-related preventive measures.
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