# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 4 Model Application and Results
### The Suspended Sediment Load (SSL) Estimation through Local Linear Regression Models
Similarly, it can be observed in Table 5 that the above-mentioned parameters were, respectively, 0.77, 0.59, 464.27 (t/day) and 15.9%, and 0.82, 0.67, 475.85 (t/day) and 17.30% for the SRC approach for the Pirna and Magdeburg-Strombrucke stations. The outcomes (Tables 5 and 6) revealed that the best LLR models are much better than the SRC model. The SRC approaches were not suitable for handling the complex sediment phenomena involved, although some adjustments could be useful to enhance the precision of the SRC approach (e.g., applying various fitted curves). The observed and predicted SSL, computed using LLR models during the test phase for the Pirna and Magdeburg-Strombrucke stations, are presented in Figures 10 and 11, respectively. of the ANN model. In this research, the best ANN model was developed using the tansig activation function in the hidden layer, purelin activation function in the output layer, Levenberg-Marquardt learning algorithm, \(10^{-5}\) as goal performance and 1000 epochs in the single-hidden-layer network. Additionally, another critical parameter is the appropriate selection of the number of nodes in the hidden layer. [PERSON], et al. [41] proposed that the hidden layer neurons should range from \(\sqrt{2n}+m\) to the value \(2n+1\). According to that study, the number of neurons in the hidden layer is increased from 3 to 11 for input combination 1 and 2 to construct several ANN models to estimate the SSL. It was observed that the performance of the ANN model was not considerably improved with the increase in the number of hidden nodes above the recommended threshold, as presented in Figure 12, which tallies with the research findings of [PERSON] and [PERSON] [71], [PERSON] [17], and [PERSON] et al. [41]. To estimate model efficiency, the ANN model was applied to all the best combinations, 1 to 5, as given in Tables 3 and 4, for each gauging station. This was followed by the computation of different statistical indices, as presented in Table 7, for the evaluation of the best ANN model at each gauging station. The results revealed that combinations 1 and 2 provided the best performance accuracy to predict the SSL for the Pirna and Magdeburg-Strombrucke stations, respectively. In combination 1 and 2, given in Table 7, the ANN structure was (5, 11, 1), describing 5, 11, and 1 input, hidden, and output neurons, for both stations, respectively. It can be observed from Table 7 that the values of R, R2, RMSE and PBIAS for testing of the ANN model (Combination 1 and Nodes 11) for the Pirna and Magdeburg-Strombrucke \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Comb.**} & \multicolumn{3}{c}{**R**} & \multicolumn{3}{c}{**R2**} & \multicolumn{3}{c}{**RMSE (t/day)**} & \multicolumn{3}{c}{**PBIAS (\%)**} \\ \cline{3-10}  & & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline \multirow{6}{*}{Pirna gauging station} & 1 & 0.96 & 0.95 & 0.92 & 0.90 & 666.75 & 233.48 & \(-\)0.03 & 3.43 \\ \cline{2-10}  & 2 & 0.94 & 0.94 & 0.89 & 0.89 & 775.99 & 237.07 & \(-\)0.30 & 4.05 \\ \cline{2-10}  & 3 & 0.95 & 0.94 & 0.90 & 0.88 & 728.73 & 238.50 & \(-\)0.43 & 3.55 \\ \cline{2-10}  & 4 & 0.93 & 0.89 & 0.86 & 0.80 & 864.15 & 321.34 & 0.06 & 7.85 \\ \cline{2-10}  & 5 & 0.94 & 0.94 & 0.88 & 0.88 & 795.29 & 244.78 & 0.07 & 6.09 \\ \hline \multirow{6}{*}{Magdeburg-Strombrücke gauging station} & 1 & 0.93 & 0.89 & 0.86 & 0.80 & 623.64 & 356.06 & 0.02 & 2.38 \\ \cline{2-10}  & 2 & 0.93 & 0.91 & 0.86 & 0.82 & 618.70 & 335.71 & \(-\)0.02 & 2.53 \\ \cline{1-1} \cline{2-10}  & 3 & 0.92 & 0.89 & 0.84 & 0.79 & 660.85 & 363.31 & \(-\)0.90 & 1.41 \\ \cline{1-1} \cline{2-10}  & 4 & 0.92 & 0.89 & 0.84 & 0.80 & 665.04 & 360.21 & \(-\)0.43 & 2.89 \\ \cline{1-1} \cline{2-10}  & 5 & 0.92 & 0.90 & 0.84 & 0.81 & 661.07 & 360.51 & \(-\)2.12 & 2.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The performance evaluation of the different ANN models.