## 3 Results and Discussion
### Performance Index Analysis  
The ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA models were employed to predict monthly SSL. Figure 9 was generated based on the following performance indices: RMSE, Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), and percent bias (PBIAS). In order to present the performances of these models, monthly SSL data were predicted utilizing the proposed models, and the monthly error values were computed based on the suggested performance indicator. Recall that the SSL predictions were achieved based not only on different model types but also on different input patterns as a model structure. Figure 9 was generated in this paper in order to simplify the comparison process and to present comprehensive information about the performance of each model. Since seven different model input combinations were considered, there were seven different error patterns for each model. The gradient charts were produced based on the lowest and highest error values of the attained errors, and then a classification for the error domain was considered. Finally, variations of error values were shown by varying the intensity of colors. In fact, Figure 9 was generated based on the application of each input combination and the computation of each index for each predicated SSL output. The vertical axis shows the number of input combinations: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**BA** & **Parameter Value** & **WA** & **Parameter Value** \\ \hline SP & 30 & MAXSP & 10 \\ MAXF & 0.7 & Minimum SP & 40 \\ MINF & 0.10 & Maximum number of seeds & 7 \\ MAXA & 5 & Minimum number of seeds & 1 \\ MinA & 1 & Maximum iteration & 900 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The optimum values of BA and WA parameters. Figure 9: Computation process of errors. 1. SSL (suspended sediment load) (lag time t-3: three months ago, number: 1);
2. Q (discharge), SSQ (lag time t-3: three months ago, number: 2);
3. SSQ, R (rainfall) (lag time t-3: three months ago, number: 3);
4. Q, R (lag time t-3: three months ago, number: 4);
5. SSL, Q, R (lag time t-3: three months ago, number: 5);
6. SSL, Q, R (lag time t-6: 6 months ago, number: 6); and
7. SSL, Q, R: (lag time t-9: 9 months ago, number: 7). The results for the models were compared with the observed data, and the statistical results were reported. 1. RMSE (Figure 10)  
The range of RMSE values varied from 1.5 to 5.5 ton/d. The ANFIS-BA had the best results since RMSE varied from 1.5 to 3.5 to a large extent (Figure 10). For the seventh combination, SSQ, Q, and R (time lag: t-9), it can be understood that during testing and training levels, the ANFIS-BA resulted in minimal RMSE. For the first combination, SSQ (time lag: t-3), it can be understood that during testing and training levels, the ANFIS-BA had the weakest performance among the ANFIS-BA models with different combinations. For the ANFIS-WA, the range of RMSE values varied from 2.5 to 4.5 ton/d. After ANFIS-BA, with different combinations, ANFIS-WA had the next priority for predicting SSL. For the MFNN-WA, it had the weakest performance since RMSE varied from 3.5 to 5.5. It should be mentioned that during the testing level, the MFNN-WA resulted in maximal RMSE (5.5). In all models, the seventh and first combination had the best and weakest performances, respectively. Figure 10: RMSE computations for different combinations. RSR (Figure 11)  
RSR range varied from 0.05 to 0.25 (Figure 11). The small values showed the best performance of the models. For the ANFIS-BA, the RSR range varied from 0 to 0.60. Thus, it had the best performance among the models. After the ANFIS-BA, the ANFIS-WA and MFNN-BA had the best and weakest performances, respectively. The fifth, sixth, and seventh combinations had the same components, but the lag time was different for those models. It can be seen (Figure 9) that increasing the lag time improved the results for all models.