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

term memory (LSTM) network to predict temperature for both ultra short-term and short term period(hourly and one day ahead). The results showed that the LSTM model was able to efficiently predict the temperature for both the time scales. However, the LSTM has several dis- advantages, such as it requires longer time and more memory to train. Besides, its parameters are difficult to assign and implement and the outcomes are vulnerable to various random weight initializations. S. Salcedo-Sanz et al. [12] used the support vector regression (SVR) and multi-layer perceptron (MLP) models to predict the mean monthly air temperature. The data- set from the monitoring stations located in New Zealand and Australia was used for the model development. The results showed that the SVR model provided the best accuracy in tempera- ture prediction. Overall, very few studies based on daily temperature prediction have been conducted for regions with continental climatic conditions Therefore, the main objective of this study is to forecast air temperature over a continental climate case study which is in North America. Two-time scales are adopted in this study, daily and weekly. For fulfilling this task, four Data-driven models i.e., Support Vector Regression (SVR), Regression Tree (RT), Quan- tile Regression Tree (QRT), and Gradient Boosting Regression (GBR) have been applied. These models have been used to predict one-day and one-week temperature ahead depending on the past temperature values for both time scales (weekly and daily). Comprehensive com-

parisons supported by statistical measures and comparative figures have been applied to select the most efficient models. 2. Methodology 2.1 Case study North Dakota is located in the middle of North America and is subjected to extreme climate conditions, with hot summers and cold winters. Due to its inland location and proximity to both the North Pole and the Equator, which are almost equal, there are noticeable temperature fluctuations. Furthermore, it has been observed that the temperature varies extremely from season to season, which may be responsible for the changes in weather throughout the time [42]. Since North Dakota has a continental climate, forecasting the patterns of meteorological parameters is a challenging task. The difficulty in simulating weather parameters in such region may be due to the nature of the fluctuating climate during the seasons. Tables 1 and 2 show the statistical characteristics of the minimum, mean, average, standard deviation, and skewness of the daily and weekly air temperature values at the Crary Table 1. Statistical characteristics of Crary station: Daily scale. Month/statics Min Average Max St. D Skewness Jan -35.50 -13.018 4.12 7.905 -0.194 Feb -32.55 -12.847 5.70 7.695 0.015 Mar -28.19 -4.191 14.85 7.465 -0.349 Apr -11.80 4.542 18.38 5.919 -0.320 May -1.67 11.860 24.87 5.072 -0.008 Jun 5.15 17.944 30.56 3.626 -0.101 Jul 12.28 20.799 27.65 2.916 -0.340 Aug 9.19 19.408 28.28 3.248 0.147 Sep 0.66 14.598 27.89 4.555 -0.078 Oct -8.15 6.098 22.86 5.436 0.137

Nov -19.79 -2.479 12.81 6.430 -0.165 Dec -30.83 -10.901 5.00 7.147 -0.320 https://doi.org/10.1371/journal.pone.0277079.t001 PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 3 / 31