# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
###### Abstract  
A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirma and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R\({}^{2}\) of 0.92 and a PBIAS of \(-0.59\%\). Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal. s _Applied_  
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individual erosional processes that contribute to sediment transport with any degree of certainty, let alone the final sediment load entering a river from the watershed. Therefore, the precise estimation and prediction of the SSL in a drainage basin has been a difficult task for hydrogeologists, environmentalists, and hydraulic engineers due to the aforementioned complex phenomena. The scientific literature provides us with a large number of relationships, proposed by different researchers to estimate the sediment rate. These equations require several empirical parameters, depending on field conditions, for the precise computation of sediment load [11]. However, notwithstanding the availability of a large number of equations for the computation of sediment load, it is still a herculean task for an engineer to identify the appropriate equation for a particular river. Furthermore, none of these equations enjoys universal acceptability in the engineering community as far as the forecast of sediment transport rates in rivers is concerned. Historically, several studies have been carried out to model sediment transport processes. Generally, mathematical models that require a large amount of input data and a prolonged computation time are used [12; 13]. To overcome these complexities, numerous studies have been performed to simplify the complex phenomena by adopting practical approaches or methods that do not entail a physical mechanism. In this context, standard time-series approaches such as conventional sediment rating curves (SRC), multi-linear regression (MLR), and auto-regressive integrated moving average (ARIMA) are usually employed to predict hydro-metrological variables, as well as SSL [14; 15; 16; 17; 18]. However, the main drawback of the standard time series approaches is that they consider linear approaches for analyzing stationary data and are limited to capturing the nonlinearity and non-stationary features of the hydro-sedimentological dataset. Over the two last decades, thanks to technological advancements, soft computing techniques have increasingly been employed in hydro-sedimentological studies and have effectively been employed as an alternative modelling tool [19; 20; 21]. In this regard, artificial neural networking (ANN), and hybrid wavelet and neural networking (WANN) are the most popular techniques that have been applied. The local linear regression model (LLR) is based on a non-parametric technique that is employed for the prediction of nonlinear time series [22].