# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 4 Model Application and Results
### Comparison of Different Models Performance  
The best models were selected after applying various techniques and then compared among themselves in order to determine the single most accurate model based on different performance evaluation indices i.e., R, R\({}^{2}\), RMSE and PBIAS, for the prediction of daily SSL in rivers. It was observed that the WANN models outperformed the other best LLR and ANN models and the SRC approaches in SSL prediction SSL for both gauging stations, as presented in Figure 17.  
Furthermore, the results of different performance evaluation parameters of various models for both gauging stations are shown in Table 9. It is revealed that the best WANN models for the Pirma and Magdeburg-Strombrucke gauging stations had a testing value of R, R\({}^{2}\), RMSE and PBIAS as 0.97 and 0.95, 0.95 and 0.90, 160.26 and 248.26 t/day and \(-0.73\) and \(-0.46\%\), respectively, and were better than the the best ANN and LLR models, as well as those from the SRC, FCF and SCF approach (Table 9). Overall, the results indicated that the WANN models for both stations had higher values of the R, R\({}^{2}\) and lower values of RMSE and PBIAS. This established that they were more accurate among the other models in the prediction of the daily SSL in rivers. Further, the list of models, in decreasing order  
Figure 16: Observed versus predicted SSL of the best WANN model for the Magdeburg-Strombrücke station in a testing phase. Figure 15: Observed versus predicted SSL of the best WANN model for the Pirma station in a testing phase. of accuracy in the prediction of daily SSL, is as follows: WANN, ANN, LLR model and SRC approaches. The model efficiency was also assessed with regard to its ability to estimate the peak SSL values. Usually, the peak SSL values are considered the key aspect in any river structure management as the design is based on those values. In this regard, the peak values were taken by considering the threshold of the top 5% of the data from the original SSL time series. The performance of different models was assessed through different statistical indices, i.e., R\({}^{2}\), RMSE and PBIAS, and the results figure in Table 10. As per the latter, the WANN models, for both stations, were more competent than the LLR, ANN and SRC models in capturing the peak SSL values. This research has served to establish the excellent performance of the ANN and the coupled ANN-WT (WANN) technique over the traditional approach of SRC. It is pertinent to investigate the reasons behind this improved performance. We begin by recalling that the SRC is based on linear interpolation (no memory effect) while the hydrological processes are inherently non-linear. The latter quality implies the dependence of a given state on a set of states coming before and after that particular time, for which a predicted value of SSL is being sought. The ANN's superior performance is explained by its employment of non-linear interpolation capability. The latter comes from the training of the model when it is fed the entire time series, thus endowing it with a'memory' that helps explain its success in better predicting SSL peaks. The WANN model is basically ANN, to which  
Figure 17: Comparison of different best models for the prediction of daily SSL by Taylor diagram.