## 4 Discussion  
In this research work, twenty-four meteorological parameters have been processed to investigate their influence on solar radiation in west-central Jordan. We used the PCC to select the most significant parameters to facilitate solar radiation prediction. After selecting the parameters, ANFIS and LSTM methods are used to forecast the solar radiation and calculate the prediction RMSE for each selected parameter according to solar radiation (i.e., with PCC value \(\geq\) 0.5). The selected parameters have been then treated to study their influence on solar radiation with the changing of the seasons. Figure 8 shows the first attempt to study solar radiation forecasting in west-central Jordan, based on a five-year database relative to twenty-four meteorological parameters (from 1 January 2017, until 22 August 2021). Only parameters with a PCC value greater than 0.5 (listed in the previous section) were selected for the forecasting process by LSTM and ANFIS to obtain low and acceptable RMSE values. The parameters that strongly correlate with the solar radiation (PCC in the range 0.98 \(\div\) 1) are the solar radiation itself, direct short-wave radiation, and diffuse short-wave radiation. As for the solar radiation, ANFIS provides a low RMSE of 0.04 and an LSTM of 0.07. As regards parameters with a PCC between 0.5 and 0.8, the LSTM method certainly performs better, providing lower and acceptable RMSE values; for example, for the temperature parameter (PCC = 0.8), LSTM gives a low RMSE of 0.35, while ANFIS gives a much higher value, equal to 0.6. The sunshine duration, soil temperature and cloud cover have a low influence on solar radiation (PCC \(\leq\) 0.5) because Jordan is poor in rain and cloud. Figure 12: Spring season ranking between different meteorological parameters based on Pearson Correlation Coefficients and comparative performance of LSTM and ANFIS models with significant parameters selection (PCC \(\geq\) 0.5) (dataset from 21 March 2021 to 20 June 2021). As for 2020 summer forecasting (Figure 9), the parameters that strongly correlate with solar radiation having PCC values between 0.98 and 1, are the solar radiation itself, direct and diffuse short-wave radiation, and temperature. Other parameters have an average correlation (PCC between 0.5 and 0.8) with solar radiation, such as the sunshine duration, growing degree days at 2 m elevation, vapor pressure deficit, and relative humidity. In particular, the sunshine duration has a remarkable influence in summer, stronger than other parameters, whereas it has no noticeable influence in other seasons. As for the 2020 autumn season, the parameters with PCC \(\geq\) 0.5 selected for LSTM and ANFIS analysis are shown in Figure 10. In more detail, parameters that strongly correlate with the solar radiation (i.e., PCC values between 0.98 and 1) are the solar radiation itself, and direct and diffuse short-wave radiation. Other parameters with significant correlation (PCC between 0.5 and 0.8) are temperature, vapor pressure deficit, growing degree days at 2 m elevation, relative humidity, sunshine duration, and evapotranspiration. Notably, the temperature and sunshine duration parameters have less influence in the autumn (PCC equal to 0.8 and 0.55 respectively) than the summer season with a PCC of 0.98 and 0.7, respectively. In comparison, evapotranspiration has more influence in autumn than summer and significantly influences solar radiation. As for the 2020 winter season, the parameters with the highest correlation (PCC in the range 0.95 \(\div\) 1) are the solar radiation, direct and diffuse short-wave radiation. Other parameters with average correlation (PCC between 0.5 and 0.8) are the temperature, evapotranspiration, vapor pressure deficit 2 m, relative humidity, growing degree days at 2 m elevation, and sunshine duration, listed by decreasing PCC values (Figure 11). Notably, the temperature has a lower influence (PCC = 0.8) than the summer season, with a PCC of 0.98.