## 5 Results and Analysis
### Optimal Lags-Based
Figure 8: Prediction capability of the eight models in test case (**a**) ELM, (**b**) KELM, (**c**) PSO-ELM, (**d**) EO-ELM, (**e**) DNN, (**f**) SVR, (**g**) ANN, and (**h**) GBM for the catchment Fal at Tregony. Figure 9: Test case observe vs. predicted runoff of (**a**) EO-ELM and (**b**) PSO-ELM for catchment Fal at Tregony. Figure 11 (refer supp) and Figure 12 (refer supp) exhibit better prediction performance of EO-ELM in train (Figure 11d) and test (Figure 12d) cases, respectively, compared to other models with their linear equations for predicted and measured values. The Taylor plot (Figure 13a,b) confirms the better prediction performance of EO-ELM in terms of statistical comparison of all models. Figure 10: Statistical comparison for all models for catchment Fal at Tregony: (**a**) train, (**b**) test. In the initial period (PACF-based selected lags) of model development, models give a distinct predictive performance for the two different catchment sizes, and characteristics result in hydrological responses to precipitation. The different values of statistical parameters of the datasets shown in (Table 2). However, the EO-ELM performed better in the large catchment Teifi at Glanteifi (R\({}^{2}\) = 0.935) which has a sensibly natural flow regime with minor flow abstractions due to the upland reservoirs and negligible agricultural demands compared to the catchment Fal at Tregony (R\({}^{2}\) = 0.91) which has moderate modification to flow due to steep topography, increasing runoff from effluent returns, and runoff reduces by industrial and agricultural abstractions that might be due to smaller watershed where the smaller catchment characteristics is more sensitive than larger watershed. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & **MAE** & **MAPE** & **NSE** & **R2** & **RMSE** & **VAF** & **TOTAL** \\ \hline \multirow{4}{*}{ELM} & Train & 4.29 & 16.20 & 0.93 & 0.93 & 8.64 & 92.97 & \\ \cline{2-9}  & Rank & 3 & 5 & 3 & 3 & 3 & 3 & 20 \\ \cline{2-9}  & Test & 5.15 & 15.02 & 0.95 & 0.93 & 10.81 & 92.60 & \\ \cline{2-9}  & Rank & 4 & 4 & 2 & 3 & 3 & 3 & 19 \\ \hline \multirow{4}{*}{KELM} & Train & 4.06 & 16.32 & 0.93 & 0.93 & 8.37 & 93.39 & \\ \cline{2-9}  & Rank & 6 & 4 & 4 & 4 & 4 & 5 & 27 \\ \cline{2-9}  & Test & 4.79 & 14.93 & 0.95 & 0.93 & 10.47 & 93.06 & \\ \cline{2-9}  & Rank & 6 & 5 & 6 & 6 & 6 & 6 & 35 \\ \hline \multirow{4}{*}{PSO-ELM} & Train & 3.64 & 11.26 & 0.94 & 0.94 & 7.87 & 94.16 & \\ \cline{2-9}  & Rank & 7 & 8 & 7 & 7 & 7 & 6 & 42 \\ \cline{2-9}  & Test & 4.59 & 12.44 & 0.95 & 0.93 & 10.45 & 93.10 & \\ \cline{2-9}  & Rank & 7 & 7 & 7 & 7 & 7 & 7 & 42 \\ \hline \multirow{4}{*}{EO-ELM} & Train & 3.58 & 11.77 & 0.95 & 0.95 & 7.64 & 94.50 & \\ \cline{2-9}  & Rank & 8 & 7 & 8 & 8 & 8 & 8 & 47 \\ \cline{2-9}  & Test & 4.46 & 11.67 & 0.95 & 0.94 & 10.18 & 93.46 & \\ \cline{2-9}  & Rank & 8 & 8 & 8 & 8 & 8 & 8 & 48 \\ \hline \multirow{4}{*}{DNN} & Train & 4.17 & 17.99 & 0.94 & 0.94 & 8.04 & 94.28 & \\ \cline{2-9}  & Rank & 5 & 2 & 6 & 6 & 6 & 7 & 32 \\ \cline{2-9}  & Test & 5.17 & 16.47 & 0.95 & 0.93 & 10.97 & 93.06 & \\ \cline{2-9}  & Rank & 3 & 2 & 2 & 2 & 2 & 5 & 16 \\ \hline \multirow{4}{*}{SVR} & Train & 5.14 & 12.45 & 0.77 & 0.81 & 15.56 & 77.96 & \\ \cline{2-9}  & Rank & 2 & 6 & 1 & 1 & 1 & 1 & 12 \\ \cline{2-9}  & Test & 7.56 & 13.78 & 0.82 & 0.82 & 20.11 & 76.18 & \\ \cline{2-9}  & Rank & 1 & 6 & 1 & 1 & 1 & 1 & 11 \\ \hline \multirow{4}{*}{ANN} & Train & 4.29 & 16.8 & 0.93 & 0.94 & 8.31 & 93.49 & \\ \cline{2-9}  & Rank & 4 & 3 & 5 & 5 & 5 & 4 & 26 \\ \cline{2-9}  & Test & 5.1 & 16.28 & 0.95 & 0.93 & 10.63 & 92.82 & \\ \cline{2-9}  & Rank & 5 & 3 & 4 & 4 & 4 & 2 & 22 \\ \hline \multirow{4}{*}{GBM} & Train & 5.98 & 41.37 & 0.89 & 0.90 & 10.43 & 89.89 & \\ \cline{2-9}  & Rank & 1 & 1 & 2 & 2 & 2 & 2 & 10 \\ \cline{1-1} \cline{2-9}  & Test & 6.13 & 34.71 & 0.95 & 0.93 & 10.55 & 92.93 & \\ \cline{1-1} \cline{2-9}  & Rank & 3 & 1 & 5 & 5 & 5 & 4 & 24 \\ \hline \hline \end{tabular}
\end{table}