## 1 Introduction
### Ionospheric Total Electron Content (TEC)  
The ionosphere is a region of Earth's upper atmosphere, extending (approximately) between 60 and 1000 km, where high energy extreme ultra-violet (EUV) and X-ray solar radiation ionizes the atmospheric atoms and molecules, forming a layer of free electrons [26; 34]. Other phenomena, such as solar flare, geomagnetic storms, energetic charged particles, and cosmic rays also have an ionizing effect and can contribute to the ionospheric total electron density concentration [27; 28; 35; 36; 37; 38]. The ionosphere structure and electron peak density changes significantly with altitude, latitude, longitude, universal time, season, solar cycle, and certain solar and geomagnetic activity, making it dynamically variable, thus causing it to be a key source of errors for technologies, such as the global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) [28; 35; 36; 37; 38]. Spectral variability of solar radiation along with density gradients of several elements in the atmosphere create regions within the ionosphere, called the D, E, and F-layers [39]. The F2 layer, extended between about 250 and 800 km [40], has the highest concentration of free electron and ions in the atmosphere and is considered the most dynamic plasma layer in the ionosphere [41].  
GNSS technology is a unique tool for continuous monitoring of the ionosphere state on a global scale [26]. It enables the estimation of the total electron content (TEC) of the global ionosphere/plasmsphere stretching up to a height of about 20,000 km [42]. GNSS is based on the accurate assessment of the travel time of the radio signals transmitted from satellites high above the earth's surface [36]. As these radio signals propagate through the earth's atmospheric layers (mainly along the troposphere and ionosphere) they are significantly affected by the physical characteristics of these layers and, thus, the propagation speed is reduced [43]. The extent of the delay depends largely on the temperature, pressure, and water vapor distribution, which differ considerably in space and time [44; 45; 46; 47; 48; 49]. In contrast to the nondispersive tropospheric interaction, the speed at which radio signals propagate at a particular height through the ionosphere is constrained by the free electron density concentration in surrounding areas [50], and the radio signal phase speed is essentially increased by the existence of free electrons. As a result, the radio signal phase accelerates as it travels through the layer, which is manifested as an early arrival at the receiver antenna compared with a complete vacuum propagation time [51]. This early arrival is commonly defined as a phase advance. The total vertical ionospheric range error within a traveling radio signal is assessed by integrating the free electron density concentration through the entire path from the satellite to the ground receiver and, consequently, is proportional to the TEC in the ionosphere [38], where one TEC Unit (TECU) is defined as the total number of free electrons in a cylinder with a base area of 1 m\({}^{2}\), which is equal to \(10^{16}\frac{\text{electrons}}{\text{m}^{2}}\). The outline of the paper is as follows. Section 2 presents the motivation for using a machine learning technique with GPS ionospheric TEC data and gives a potential explanation for TEC enhancement as a possible earthquake precursor. Section 3 describes the datasets and methodology we used in this study. Section 4 presents the SVM model performance using different skill scores. The discussion and conclusions follow in Sections 5 and 6, respectively.