Data-driven models for atmospheric air temperature forecasting at a continental climate region

weekly temperature prediction. For daily forecast, all models provide satisfactory results. Nev- ertheless, the RF provides the best performance, with RMSE ranging from 1.807 to 2.824, R ranging from 0.978 to 0.991, U ranging from 0.065 to 0.101, and MAE ranging from 1.389 to 2.157, followed by the QRT model with RMSE ranging from 2.0874 to 3.1830, R ranging from 0.9722 to 0.9882, U ranging from 0.0745 to 0.1134, and MAE ranging from 1.3492 to 2.24, and RT model with RMSE ranging from 2.3687 to 3.09, R ranging from 0.9738 to 0.9849, U rang- ing from 0.0849 to 0.1106, and MAE ranging from 1.7951 to 2.374. While GBR and SVR mod- els came last with RMSE ranging from 3.4233 to 3.6633 and from 3.6821 to 3.7887, R ranging from 0.963 to 0.9677 and from 0.9604 to 0.9626, U ranging from 0.1229 to 0.1328 and from 0.1321 to 0.1353, and MAE ranging from 2.6659 to 2.8746 and from 2.8369 to 2.8965, respec- tively. Furthermore, increasing the number of input lags increases the accuracy of the QRT, RT, and SVR models. Here the QRT model reaches the optimum accuracy when the input lag is nine (QRT − M9), the RT model with eight input lags (RT − M8), SVR model with ten input lags (SVR − M9) and the GBR model with five input lags (GBR − M5). On the contrary, the RF model only requires one input lag to reach its optimum performance RF–M1. For weekly temperature prediction, all models provide satisfactory predictions reaching the best performance with the QRT model with RMSE ranging from 2.5069 to 3.7157, R ranging

from 0.9589 to 0.9817, U ranging from 0.0932 to 0.11386, and MAE ranging from 1.6300 to 2.5880, followed by RF model with RMSE ranging from 2.799 to 3.647, R ranging from 0.960 to 0.977, U ranging from 0.105 to 0.136, and MAE ranging from 2.116 to 2.787. Moreover, increasing the input lags for weekly prediction improves the accuracy of QRT, RF, RT, and SVR models which allows them to reach their optimum accuracy (QRT − M16, RF − M16, Table 4. The performance of the proposed models for daily temperature prediction: Training phase. Models GBR QRT RT SVR RF RMSE R U MAE RMSE R U MAE RMSE R U MAE RMSE R U MAE RMSE R U MAE M1 3.663 0.963 0.132 2.829 3.183 0.972 0.113 2.24 3.09 0.974 0.111 2.374 3.789 0.96 0.135 2.897 1.807 0.991 0.065 1.389 M2 3.573 0.965 0.128 2.771 2.763 0.979 0.099 1.878 2.851 0.978 0.102 2.171 3.745 0.961 0.134 2.886 2.824 0.978 0.101 2.157 M3 3.553 0.965 0.128 2.759 2.621 0.981 0.094 1.768 2.75 0.979 0.099 2.123 3.734 0.961 0.134 2.873 2.756 0.979 0.099 2.117 M4 3.521 0.966 0.127 2.734 2.357 0.985 0.084 1.562 2.529 0.983 0.091 1.923 3.724 0.962 0.133 2.864 2.521 0.983 0.090 1.919 M5 3.423 0.968 0.123 2.666 2.281 0.986 0.081 1.51 2.486 0.983 0.089 1.896 3.716 0.962 0.133 2.861 2.488 0.983 0.089 1.895 M6 3.472 0.967 0.125 2.701 2.267 0.986 0.081 1.49 2.488 0.983 0.089 1.892 3.709 0.962 0.133 2.853 2.497 0.983 0.090 1.904 M7 3.488 0.967 0.126 2.72 2.109 0.988 0.075 1.37 2.4 0.984 0.086 1.817 3.698 0.962 0.133 2.851 2.366 0.985 0.085 1.792

M8 3.639 0.965 0.133 2.875 2.094 0.988 0.075 1.361 2.369 0.985 0.085 1.795 3.692 0.962 0.132 2.841 2.368 0.985 0.085 1.800 M9 3.503 0.966 0.126 2.734 2.087 0.988 0.074 1.349 2.387 0.985 0.086 1.812 3.682 0.963 0.132 2.837 2.369 0.985 0.085 1.803 https://doi.org/10.1371/journal.pone.0277079.t004 Table 5. The performance of the proposed models for weekly temperature prediction: Training phase. Models GBR QRT RT SVR RF RMSE R U MAE RMSE R U MAE RMSE R U MAE RMSE R U MAE RMSE R U MAE M10 3.898 0.954 0.146 3.02 3.716 0.959 0.139 2.588 3.663 0.96 0.137 2.792 4.648 0.935 0.174 3.607 3.647 0.960 0.136 2.787 M11 3.797 0.957 0.143 2.971 3.486 0.964 0.13 2.379 3.522 0.963 0.132 2.713 4.607 0.936 0.173 3.584 3.458 0.965 0.130 2.669 M12 3.837 0.956 0.144 2.992 3.256 0.969 0.121 2.215 3.578 0.962 0.135 2.72 4.598 0.936 0.173 3.572 3.455 0.965 0.130 2.668 M13 3.333 0.967 0.125 2.605 2.898 0.975 0.108 1.93 3.167 0.971 0.119 2.398 4.543 0.938 0.17 3.528 3.126 0.972 0.117 2.400 M14 3.125 0.971 0.117 2.428 2.756 0.978 0.103 1.827 3.036 0.973 0.114 2.332 4.473 0.94 0.167 3.459 3.075 0.973 0.115 2.341 M15 3.466 0.965 0.131 2.72 2.645 0.98 0.099 1.737 3.019 0.974 0.113 2.302 4.422 0.941 0.166 3.383 3.036 0.973 0.114 2.305 M16 3.392 0.967 0.128 2.651 2.507 0.982 0.093 1.63 2.816 0.977 0.105 2.142 4.308 0.944 0.162 3.303 2.799 0.977 0.105 2.116 M17 3.827 0.962 0.147 3.044 2.853 0.976 0.106 1.880 2.822 0.977 0.106 2.144 3.532 0.963 0.131 2.689 2.737 0.978 0.102 2.109

https://doi.org/10.1371/journal.pone.0277079.t005 PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 14 / 31