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

RESEARCH ARTICLE Data-driven models for atmospheric air temperature forecasting at a continental climate region Mohamed Khalid AlomarID1, Faidhalrahman Khaleel1, Mustafa M. Aljumaily1, Adil Masood2, Siti Fatin Mohd Razali3, Mohammed Abdulhakim AlSaadi4, Nadhir Al- AnsariID5*, Mohammed Majeed HameedID1,3* 1 Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq, 2 Department of Civil Engineering, Jamia Millia Islamia, New Delhi, India, 3 Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Selangor, Malaysia, 4 Natural and Medical Sciences Research Center, University of Nizwa, Nizwa, Oman, 5 Civil Engineering Department, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden * m.majeed@uoa.edu.iq, mohmmag1@gmail.com (MMH); nadhir.alansari@ltu.se (NAA) Abstract Atmospheric air temperature is the most crucial metrological parameter. Despite its influ- ence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important informa- tion that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied

to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. 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 vali- dation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592˚C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745˚C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substan- tially influence the predictive models’ efficacy. Furthermore, the second scenario is con- ducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the stud- ied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies. PLOS ONE

PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 1 / 31 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Alomar MK, Khaleel F, Aljumaily MM, Masood A, Razali SFM, AlSaadi MA, et al. (2022) Data-driven models for atmospheric air temperature forecasting at a continental climate region. PLoS ONE 17(11): e0277079. https://doi. org/10.1371/journal.pone.0277079 Editor: Yangyang Xu, Texas A&M University, UNITED STATES Received: April 10, 2022 Accepted: October 18, 2022 Published: November 3, 2022 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0277079 Copyright: © 2022 Alomar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data are within the paper. Funding: The authors received no specific funding for this work.