# Forecasting Ionospheric foF2 Based on Deep Learning Method
## 2 Models and Data
### Neural Network Models
#### 2.1.2 Input and Output Parameters  
Our models are obtained by adjusting the network parameters and training with specified input and output sample data sets. The input factors for our models are a set of data related to the ionospheric variabilities in terms of time, space, solar and geomagnetic activity, and other corresponding factors, which are chosen based on the previous experience of parameters known to cause variations in foF2. The output of our models is usually the parameters to be forecasted. The input factors are chosen for our models are as follows:  
(1) Local time (LT): The most remarkable feature of ionospheric electron density is its diurnal variation, which is described by Local time. Also, studies have shown that the occurrence of ionospheric storms is depended on the local time [34]. Therefore, local time in the range of 0-23 is utilized as an input factor of our models;  
(2) day number (DOY): The previous studies show that the number of days in a year also affects the variations of ionospheric foF2 [35; 36], so DOY is chosen as an input parameter of our models;  
(3) Solar zenith angle (CH): Due to the Earth's tilt and rotation around the sun, the F2 layer electron density varies with time of the day and with season according to the solar zenith angle, so the solar zenith angle CH is considered as an input factor of our models;  
(4) Geographic coordinates: Studies have shown that there are spatial correlations of foF2 [37], and the spatial characteristics of ionospheric foF2 are reflected by the geographical longitude and latitude of the ionosonde stations. Therefore, the geographical latitude (LAT) and the geographical longitude (LON) are adopted as two input parameters in our models;  
(5) Geomagnetic activity: The geomagnetic indices are adopted to represent the geomagnetic activity. Studies have shown that the 3-h _ap_ of the geomagnetic planetary index can be easily obtained, but any integration is limited by the 3-h resolution and cannot be usefully applied to events with time scales of less than several hours [38]. Therefore, the indices \(ap(\tau)\) are calculated by a time-weighted accumulation series derived from the geomagnetic planetary index \(ap\). The formula is defined as,  
\[ap(\tau)=(1-\tau)[ap_{0}+(\tau)ap_{-1}+(\tau^{2})(ap_{-2})+\dots] \tag{1}\]  
here, \(ap_{0}\) is the initial value of the magnetic index, \(ap_{-1}\), \(ap_{-2}\), \(\dots\) represent the values of 3 h before, 6 h before, respectively; \(\tau\) is a persistence factor of attenuation multiplier ranging from 0 to 1, which determines the degree to which \(ap(\tau)\) depends on previews \(ap\), the larger \(\tau\) is, the more \(ap(\tau)\) depends on the past value. Based on [PERSON]'s work, we chose \(ap(\tau)\) with \(\tau=0.8\) and \(ap_{-10}\) for the calculation of \(ap(\tau)\) as an input factor in our models;  
(6) Sunspot number (SSN): Several indices have been developed in the past to map the response of the peak density in the ionosphere foF2 to variations in solar output. The sunspot number became a standard index used by many ionosphere models as standard input, including the International Radio Consultative Committee foF2 model [39]. Therefore, the number of sunspots SSN is used as an input factor of our models;  
(7) Solar activity: The existence of a strong relationship between foF2 and solar activity has been well studied in previous work [32], and the effect of the 27-day solar cycle in the ionosphere has been investigated [40; 41]. So, a 27-day running means of solar radio flux F10.7 cm is adopted as an input factor in our models, which is provided by the National Geophysical Data Center of the National Oceanic and atmospheric administration (NOAA) with the time resolution of one day. (8) Neutral winds: The variation and distribution of the ionospheric F2 layer ionization are affected by the thermospheric wind [42].