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

1. Introduction It is well-known that numerous meteorological and ecological events, human life, and crops in agricultural areas are significantly influenced by climate conditions as well as several factors related to the environment’s physical conditions. The natural resources that provide humans basic needs and opportunities for social and economic development are part of the physical environment, including land, air, and water. A clean and healthy environment is one of the essential principles that should be preserved and protected [1]. The temperature parameter is seen as the most influential parameter out of all meteorological parameters, which reflects the effect of climate change on earth and its surrounding atmosphere. Recently, climate change has caused extreme natural phenomena such as heat waves, severe winters, heavy snowfall, and droughts worldwide, leading to environmental and health crises [2–6]. Air temperature prediction helps meteorologists to know the likelihood of hurricanes and floods in an area [7]. Various meteorological parameters such as rainfall, humidity, atmospheric pressure, wind speed, solar energy, and soil temperature are significantly correlated to air temperature [8]. Moreover, air temperature is one of the most influential factors in evapotranspiration, which is vital for managing water resources and agricultural activities [9]. Accurate air temperature prediction is substantial in many decision-making sectors, such as energy, agriculture, trans- portation, and tourism [10]. Additionally, accurately predicting air temperature is the most

crucial aspect of environmental studies involving operational eco-environmental systems. From the industrial aspect, predicting air temperature is essential in energy management strat- egies to obtain comfortable indoor temperatures and eventually reduce the consumption of energy [11]. According to the literature, two main approaches have been used to predict air temperature: general circulation (GCM) and statistical models. The GCM is utilized to comprehend the dynamics behind climate system physical components, derive global temporal and spatial changes and make predictions based on the future forcing of greenhouse gasses and aerosols [12]. GCMs can be applied to the problem of attributing climate change from a season to a decade ahead. Conversely, statistical models attempt to determine whether climate change is externally driven by minimizing the utilization of complex climate models. They are generally more straightforward and less computationally intensive than the GCMs, and several studies have showed that the use of statistical models has produced results consistent with GCMs. Var- ious statistically based approaches have been proposed recently, several of which have been developed in the econometric literature. The statistical models can be categorized into two approaches: cointegration approaches which determine the relationship between non-station- ary and stationary times series [13], and regression approaches which evaluate the characteris- tics of time series for a given temperature data [14, 15]. However, since temperature prediction

involves high nonlinearity and dimensionality, the statistical models faced some drawbacks in capturing them [16]. Meanwhile, machine learning (ML) approaches have attracted much attention due to their superlative performance in dealing with high nonlinearity phenomena [17, 18] and solving complex problems such as drought [19–24], rainfall [25–29], evapotranspiration [30–34] and streamflow [35–38]. For example, a study was conducted in the Queensland area where ML models’ performances were compared with the Australian Predicted Ocean-Atmosphere Model (POAMA) for precipitation prediction. The POAMA model showed a significant improvement in the predictive performance of the ML modeling framework. It was reported that the performance of the neural network (NN) model was superior to POAMA in precipita- tion prediction over three regions in Queensland [39–41]. For temperature prediction, A. Sekertekin et al. [6] used the adaptive neuro-fuzzy inference system (ANFIS) and long-short PLOS ONE Air temperature forecasting: Mutli-time scale PLOS ONE | https://doi.org/10.1371/journal.pone.0277079 November 3, 2022 2 / 31 Competing interests: The authors declare that there are no conflicts of interest.