

COMPARING THE LSTM (LONG SHORT-TERM MEMORY) TO OTHER NEURAL NETWORK TECHNIQUE ON LOW COST AIR FILTER
Abstract
Key results from the implementation demonstrate that the system provides reliable real-time AQI predictions, with the LSTM model significantly outperforming traditional models. The low-cost sensors showed good correlation with reference-grade monitoring equipment, particularly for PM2.5 and PM10 measurements. For instance, PM2.5 concentrations predicted by the system closely followed World Health Organization (WHO) standards, which set the annual mean limit at 10 µg/m³ and the 24-hour mean limit at 25 µg/m³. PM10 concentrations adhered to the WHO annual mean limit of 20 µg/m³ and 1-hour mean limit of 50 µg/m³. Compared to similar works, this study on LSTM model achieved better performance. Previous studies reported higher RMSE values for PM2.5 and PM10 predictions, while our model’s training and validation MSE values were significantly lower.
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