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Table 1 Results of different models in 1 and 7-days Lags (Bold indicates the best results.)

From: Air-pollution prediction in smart city, deep learning approach

Model Batch 1 Day 7 Day
MAE RMSE \(R^{2}\) MAE RMSE \(R^{2}\)
GRU 24 9.960 16.571 0.979 12.868 21.526 0.953
32 9.541 16.609 0.978 11.055 18.122 0.974
64 10.021 17.220 0.977 12.258 19.797 0.965
128 9.842 16.904 0.978 11.899 19.560 0.970
LSTM 24 9.102 15.999 0.980 11.916 19.255 0.969
32 9.503 16.217 0.980 11.623 19.154 0.972
64 9.252 16.044 0.980 11.551 19.155 0.970
128 9.725 16.997 0.978 11.823 19.227 0.971
Bi-LSTM 24 8.947 15.710 0.982 12.204 20.050 0.966
32 8.868 15.597 0.982 11.253 18.488 0.974
64 9.561 16.380 0.980 12.055 19.323 0.969
128 9.488 16.456 0.979 11.753 18.113 0.971
Bi-GRU 24 9.907 16.859 0.978 11.488 18.854 0.969
32 9.692 16.712 0.978 11.984 19.294 0.970
64 9.192 16.196 0.981 11.631 19.289 0.969
128 9.230 16.046 0.981 11.553 19.113 0.970
CNN 24 9.663 17.062 0.978 10.693 17.962 0.973
32 9.591 16.981 0.979 11.150 18.606 0.975
64 9.261 16.667 0.979 10.621 18.431 0.975
128 9.974 17.636 0.977 10.674 18.636 0.974
CNN-LSTM 24 9.198 16.523 0.981 9.353 16.724 0.978
32 6.742 12.921 0.989 9.034 16.625 0.979
64 7.869 15.757 0.982 9.885 18.373 0.976
128 8.940 16.337 0.980 9.037 16.524 0.979
CNN-GRU 24 9.812 17.554 0.977 9.912 18.564 0.965
32 9.459 17.700 0.977 9.459 18.700 0.967
64 9.433 16.836 0.979 9.653 17.856 0.976
128 9.499 17.285 0.979 9.949 17.885 0.976