# Linking Singular Spectrum Analysis and Machine Learning for Monthly Rainfall Forecasting
## 4 Results and Discussion
From this analysis, it is clear that the RF-SSA is superior to the SSA-LSSVR in Shihmen Reservoir watershed. Figures 5 and 6 illustrate the scatterplots of the observed vs. forecasted rainfalls for (a) SSA-LSSVR, (b) SSA-RF for Deji Reservoir watershed, and 6(c) SSA-LSSVR, 6(d) SSA-RF for Shihmen Reservoir watershed during the validation for 1-, 2-, and 3-month lead-time. In Figure 5b,d, it can be seen that \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Watershed**} & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**RMSE**} & \multicolumn{3}{c}{**NSE**} \\ \cline{2-9}  & & **1** & **2** & **3** & **1** & **2** & **3** \\ \hline \multirow{4}{*}{Deji} & LSSVR & 193.58 & 192.31 & 199.45 & 0.05 & 0.07 & 0.01 \\  & SSA-LSSVR & 75.29 & 79.48 & 91.09 & 0.86 & 0.84 & 0.80 \\  & RF & 195.88 & 198.34 & 196.28 & 0.04 & 0.03 & 0.03 \\  & SSA-RF & 121.76 & 137.77 & 154.83 & 0.63 & 0.53 & 0.39 \\ \hline \multirow{4}{*}{Shihmen} & LS-SVR & 195.55 & 197.77 & 197.88 & 0.30 & 0.27 & 0.28 \\  & SSA-LSSVR & 132.81 & 120.91 & 115.71 & 0.67 & 0.73 & 0.75 \\ \cline{1-1}  & RF & 227.14 & 221.93 & 223.21 & 0.06 & 0.08 & 0.08 \\ \cline{1-1}  & SSA-RF & 98.75 & 108.88 & 119.10 & 0.82 & 0.78 & 0.74 \\ \hline \hline \end{tabular}  
* The numbers 1, 2, and 3 denote the lead times of one, two, and three months, respectively.  
\end{table} 
Table 4: Forecasting performances of the LS-SVR and SSA-LSSVR models during the validation period. the SSA-LSSVR forecasts are better than the SSA-RF forecasts in Deji reservoir watershed for 1-,2-, and 3-month lead-time. From Figure 6b,d, the SSA-LSSVR and SSA-RF perform similarly in Shihmen Reservoir watershed for 3-month lead-time. The above results show that the SSA-LSSVR outperforms the SSA-RF in Deji Reservoir watershed, whereas the SSA-RF is superior to the SSA-LSSVR in Shihmen Reservoir watershed, which may be attributed to the different rainfall characteristics of the catchment. The catchment characteristics are an important feature of all models for hydrological simulation and prediction. The performance of simulation and prediction techniques for single hydrometric stations differ according to their watershed climate zone and characteristics, and the climate conditions in different watersheds will significantly affect the effectiveness of different forecasting techniques [95]. In some cases, the behavior of the watersheds is more sensitive to the effects of rainfall patterns, so some models would perform better than others [96]. Figure 3: Time-series graphs comparing the observed and forecasted rainfalls by the (**a**) LS-SVR, (**b**) SSA-LSSVR, (**c**) RF, and (**d**) SSA-RF models during the validation period for Deji Reservoir watershed 1-month lead time. Figure 4: Time-series graphs comparing the observed and forecasted rainfalls by the (**a**) LS-SVR, (**b**) SSA-LSSVR, (**c**) RF, and (**d**) SSA-RF models for Shihmen Reservoir watershed 1-month lead time. Figure 5: Cont. Figure 5: Scatterplots of the observed vs. forecasted rainfalls by the (**a**) LS-SVR, (**b**) SSA-LSSVR, (**c**) RF, and (**d**) SSA-RF models during the validation period for Deji reservoir watershed 1-,2-, and 3-month lead time. Figure 6: Scatterplots of the observed vs. forecasted rainfalls by the (**a**) LS-SVR, (**b**) SSA-LSSVR, (**c**) RF, and (**d**) SSA-RF models during the validation period for Shihmen reservoir watershed 1-,2-, and 3-month lead time. In general, given the aforementioned differences in model performances, our findings support the view that the hybrid models are more efficient for monthly rainfall forecasting than the standard models. Furthermore, the performances of the two hybrid models (SSA-LSSVR and SSA-RF) vary in both reservoir watersheds, which might result from different hydrological behaviors (e.g., rainfall patterns). Having a prior knowledge of the rainfall characteristics (standard deviation, skewness, and kurtosis) of the studied watershed would be helpful to apply the suitable model.