# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 5 Conclusions  
Sediments carried by water are a nuisance, as they shortens the life of a reservoir, reduce the channel discharge-carrying capacity, especially to tail-end users, etc. Therefore, sediment management is the golden rule in river engineering, to which much effort and energy are directed. An important aspect of sediment management is sediment estimation, which is mostly found in a suspended form in rivers and other water bodies. This research focused on a comparison of the different means of suspended sediment estimation in rivers. This includes the traditional method, i.e., sediment rating curve (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. All the methods require extensive data from the field, which were obtained from the two river gauging stations situated on the Elbe River in Germany. The data pertain to the daily mean flow and suspended sediment concentration. The SRC represents a functional relationship between daily SSL load in tons and the river volume flow rate in power-law form. The LLR model is the most effective and reliable tool in regions of high data density in the input space, but it is not significantly effective if the datapoints are scarce and distant from the locality of the query point. The ANN is inspired by the biological (brain) neuron system, which is well-suited to the modelling of non-linear and complicated tasks such as the estimation and forecasting of rainfall, runoff and river sediment. In the WANN models, the original time series data of different input combinations for each station, selected through the Gamma test (GT), were decomposed using the discrete wavelet transform (DWT) approach and fed into the ANN models as input. The model was built based on the postulation that the present-day SSL response depends upon today's and the antecedent two days volume flow rates and the sediment concentrations of the previous day and the day before. Further, the Gamma test and M-test were performed for the identification of optimal input combinations and selection of a suitable data length for model development, respectively. Thus, by applying the Hill-climbing model-identification technique, a total of five best-input combinations were selected based on the minimum Gamma statistic and V-ratio for each station. Similarly, M-test identified an optimal training data length of around 75% of the total data for accurate model development for both stations. The goodness of fit for any model was assessed via R\({}^{2}\), RMSE and PBIAS. The SRC performed well, with an R\({}^{2}\) of 0.64, but it still overestimated the sediment load and was unable to capture the peak sediment rates, which are of great importance for design purposes. The best LLR model scored a mean R\({}^{2}\) of about 0.85, overtaking the best SRC \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{
\begin{tabular}{c} **Gauging** \\ **Station** \\ \end{tabular} } & \multirow{2}{*}{**Statistical**} & \multirow{2}{*}{**SRC**} & \multirow{2}{*}{**FCF**} & \multirow{2}{*}{**SCF**} & \multirow{2}{*}{**LLR**} & \multirow{2}{*}{**ANN**} & \multirow{2}{*}{**WANN**} \\  & & & & & & & \\ \hline \multirow{4}{*}{Pirna} & R\({}^{2}\) & 0.54 & 0.54 & 0.54 & 0.82 & 0.85 & 0.95 \\ \cline{2-8}  & RMSE (t/day) & 6254.85 & 5625.65 & 6044.63 & 2685.30 & 2449.23 & 1382.48 \\ \cline{2-8}  & PBIAS (\%) & \(-\)54.99 & \(-\)43.94 & \(-\)51.43 & \(-\)5.93 & \(-\)4.49 & \(-\)2.66 \\ \cline{2-8}  & R\({}^{2}\) & 0.21 & 0.21 & 0.21 & 0.56 & 0.63 & 0.77 \\ \cline{2-8}  & RMSE (t/day) & 4334.35 & 3820.03 & 3855.03 & 2193.03 & 2028.45 & 1528.19 \\ \cline{2-8}  & PBIAS (\%) & \(-\)53.93 & \(-\)42.49 & \(-\)43.35 & \(-\)13.30 & \(-\)11.73 & \(-\)6.09 \\ \hline \end{tabular}
\end{table} 
Table 10: A performance comparison among models in capturing peak SSL values. results. Although, it captured the peaks better than SRC, it still was unable to match the observed SSL peak concentration rates.