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

(30%). After that, the data-driven models (QRT, GBR, RF, and RF) were trained and assessed using several statistical metrics based on this data division. Furthermore, the outcomes of these models are compared with their corresponding results, which have already been dis- cussed. This technique may help observe whether there is an effect on the behavior and accu- racy of the model predictions when using recent time series data during the training process. According to the results shown in Table 10, all the predictive models’ performances are posi- tively affected using the Randomization method approach. For example, when classical data division procedure was used the RF model generates relatively higher errors (RMSE = 1.776, U = 0.063, and MAE = 1.353) and however, the prediction accuracy is slightly enhanced and the model provides lower forecasting error (RMSE = 1.697, U = 0.061, and MAE = 1.325). Overall the Randomization method has a role in improving the model capacity because it includes features related to future temperature trends in the training data used to train the sug- gested models in this work. 4. Conclusion The accuracy of the Data-Driven Models, namely RT, SVR, QRT, RF, ARIMA, and GBR, have been investigated to forecast atmospheric air temperature on different time scales (daily and weekly) using historical meteorological data. The data was collected from Cray station, located in North Dakota, USA. This region experiences a volatile continental climate throughout the

year. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for testing. Several input groups were Fig 10. The histogram and Gaussian kernel density function for daily temperature prediction during the testing phase. https://doi.org/10.1371/journal.pone.0277079.g010 PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 23 / 31