## 3 Results  
In this study, LSTM and ANFIS learning models are used to predict the amount of solar radiation available in the west region of Jordan. Twenty-four meteorological parameters are considered in the prediction process, selected for five different scenarios (i.e., referring to the five-year time interval or specific seasons, autumn, summer, spring and winter). The results show that the degree of influence of these parameters depends on seasonal variation. The forecast RMSE related to the five-year dataset is calculated for the different scenarios. The meteorological parameters are ranked based on PCC values using LSTM and ANFIS and properly selected for the different seasons. As a first result of the research work, Figure 7 depicts the forecasted solar radiation based on temperature parameter in the summer season using the LSTM model. When the temperature changes, the solar radiation changes in the same way with a prediction error of the solar radiation according to the temperature variation. The obtained RMSE value is 0.14, proving that the model accurately forecasts the solar radiation. Figure 8 provides the RMSE values related to forecasted solar radiation for the five-year dataset by employing LSTM (red line) and ANFIS (blue line) models. The RMSE values are lower for meteorological parameters with greater PCC values (close to 1) relative to solar radiation (i.e., direct short-wave radiation, diffuse short-wave radiation, and temperature). The pink area (below the red line related to the 0.5 PCC threshold) indicates the parameters \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **FIS** \\ \hline Type & Sugeno \\ And-Method & Prod: \\ Or-Method & Probor \\ DefuzzMethod & Wtaver (Weighted average of all rule outputs) \\ ImpMethod & Prod \\ AggMethod & Sum \\ Input Size & 1 \\ Output Response size & 1 \\ Rules & 7 \\ Epoch & 250 \\ Ranges of influence & 0.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training parameters for ANFIS software. Figure 7: Solar radiation forecasted in the summer season (from 21 June to 20 September 2020). set with PCC values lower than 0.5 (not significant for the forecasting process), which have therefore been excluded from the learning phase [55]. The solar radiation forecast by the ANFIS model gives a better result than the LSTM one when the PCC is high (>0.95); instead, for (0.5 \(\leq\) PCC < 0.95), the LSTM provides better results. It can be concluded that the proposed methodology gives good results, with an RMSE equal to 0.12 (by ANFIS model) for direct short-wave radiation (PCC = 0.98) up to an RMSE value of 0.32 for temperature parameter (PCC = 0.80) provided by the LSTM model. Figure 9 shows the meteorological parameters' ranking for the 2020 summer season. The selected parameters with PCC \(\geq\) 0.5 are listed below with decreasing PCC values: the direct and diffuse short-wave radiation, temperature, sunshine duration, growing degree days at 2 m elevation corrected, vapor pressure deficit at 2 m, and relative humidity. As explained above, the other parameters with a PCC value less than 0.5 were not used in the forecasting models, but discarded. Figure 8: Meteorological parameters’ ranking based on Pearson Correlation Coefficients and Comparative performance of LSTM and ANFIS models with significant parameters selection (PCC value \(\geq\) 0.5) (employed dataset from 1 January 2017, until 22 August 2021). Figure 9: Summer season ranking between the 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 June 2020 to 20 September 2020). Figure 10 shows the meteorological parameters' ranking for the 2020 autumn season.