## 4 Discussion
In contrast, the evapotranspiration parameter has a greater influence on solar radiation forecasting (PCC = 0.8) than in summer (PCC = 0.45). As for the 2021 spring season, the parameters that strongly correlate with the solar radiation (PCC between 0.95 and 1) are solar radiation itself, direct short-wave radiation, and diffuse short-wave radiation, while other parameters have PCC values between 0.5 and 0.8 (Figure 12). In this season, the temperature has less influence than in the summer season, with a PCC of 0.8 compared to 0.98 in summer. Evapotranspirationpiration and sunshine duration were not selected for the forecasting process because their PCC is less than 0.5 (0.48 for both parameters in the spring season compared to PCC values of 0.80 in winter and 0.7 in summer for the two parameters). Based on the previous analysis of solar radiation forecasting in the different seasons, the significant parameters have been grouped into two classes depending on the determined PCC value (Table 4). From the results reported in Section 3 (shown in Figures 8-12), as for first-class parameters (i.e., likely linear correlation) as inputs to the solar radiation forecasting process, the ANFIS model gives low RMSE values compared to LSTM. Instead, concerning second class parameters (PCC ranges from 0.5 to 0.8, i.e., unlikely linear correlation), the LSTM performs better than the ANFIS method based on the RMSE values for the forecasting process performed in all four seasons. This work provides the following contributions: firstly, the parameters of solar radiation, direct short-wave radiation, diffuse short-wave radiation, and temperature always have a very high degree of influence on solar radiation forecasting based on results obtained with both the complete five-year dataset as well as the seasonal ones. Secondly, evapotranspiration, sunshine duration and humidity showed a remarkable influence in west-central Jordan; instead, other parameters like cloud cover, snowfall amount, wind speed, and total precipitation amount have no influence in Jordan on the solar radiation prediction. Due to Jordan's geographical location with relatively high values of daily solar irradiance, the average sunshine duration is approximately 300 days a year, with average daily sunshine of 9.07 h. The PCC values' ranking of the different parameters with respect to solar radiation can change with the season. For example, regarding the temperature, the obtained PCC values with respect to solar radiation are 0.99 in summer, 0.85 in spring, 0.83 in autumn, and 0.81 in winter; this means that in summer, the temperature has a higher correlation with solar radiation than other seasons. The RMSE of forecasting by LSTM equals 0.14 in summer and only 0.5 in winter, as the PCC between the temperature and solar radiation decreases. \begin{table}
\begin{tabular}{c c c c} \hline
\begin{tabular}{c} Growing degree days at \\ 2 m elevation \\ \end{tabular} & \(0.5\div 0.8\) \\ \cline{2-4}  & Relative humidity at 2 m \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters that influence solar radiation in the different seasons (classified into two different classes according to PCC value) and used for the solar radiation forecasting process by LSTM and ANFIS methods. Table 5 shows the five criteria researchers must consider in order to build a reliable solar radiation forecasting model; namely, test location, time duration of the study, employed parameters as inputs, machine learning models, and evaluation criteria. The experimental data were collected worldwide, whereas the last three studies were conducted in Jordan [29; 30; 31]. For the published research articles, the time-length of studied parameters in the analysis that influence the solar radiation prediction ranged from 5 months to 20 years. The employed parameters varied in number and type. In the presented research work, twenty-four parameters have been involved for the first time, a significantly higher number than in the scientific literature to date.