# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
## 2 Methodology
### Limitation and Uncertainties in SSL Prediction  
The uncertainties in the description of any physical process arise either from limitations in measurement techniques, imperfect knowledge of the process being modelled, i.e., epistemic uncertainty, or both. In our case of SSL prediction through the soft computing technique, both of these sources are present. The measurement uncertainty comes into play through the accuracy of the input time-series data regarding SSL being fed to the model. The SSL comprises the bed sediment load component in suspension and the so-called wash load. The latter has no nevus, with the sediment forming the channel bed, and consists of fine-sized particles originating from the catchment through sheet erosion. In rivers, sizes finer than 0.0625 mm are approximated as wash load. The SSL measurement is a delicate affair due to the variation in flow velocity and suspended sediment concentration, along with the depth. In principle, the SSL can be found by measuring the flow velocity and sediment concentration at a sufficient number of points over the cross-section and summing the product. In the field, the SSL is usually measured by an instrument known as a depth-integrating sampler. It is lowered at a uniform rate into the water from the surface to the channel bed, and then pulled back at the same rate. The sample concentration, thus collected, represents the mean concentration of the mixture, and when multiplied by the unit discharge, yields the sediment discharge per unit width. It can be appreciated that if there is a significant deviation from the mean sediment concentration over the vertical, there would be a corresponding error induced in the SSL. Usually, this deviation is maximal in streams where sand proportion (size > 0.0625 mm) forms the predominant part of the suspended load, while it is minimal in streams where silt and clay form the bulk of the suspended load. Thus, the raw input time series of SSL has to be viewed against the backdrop of the type of sediment load carried by the river.  
As regards uncertainty and limitations in coupled WANN models (epistemic uncertainty), some drawbacks can be associated with this type of modelling. The WT analysis tool decomposes the primary time series data into different sub-series data with detailed hidden information. Therefore, a large volume of data is fed to the ANN model as input, which can cause model complexity, errors, non-convergence, and overtraining. Owing to the large number of time series input samples and the aforementioned WANN drawbacks, the selection of an appropriate input sample and data length for the training of the ANN models are important to avoid errors and complexity in the dataset.  
A further question to ponder is the efficacy of the present WANN model is its ability to predict SSL in highly turbid rivers. Generally, rivers in a temperate region carry less sediment as compared to arid and semi-arid regions. Under such conditions, it is natural to wonder about the efficacy of the WANN technique, given the difference in sediment loading. However, it is suggested that this issue is more pertinent in physical models of flow and sediments than in data-driven models. A great majority of the former models only account for the water phase and assume that sediment proportion is very small compared to the solvent. Therefore, in case of a highly turbid flow, their predictions are less accurate, as turbulent eddy viscosity differs markedly for the sediment and water phases. However, in our case, the only physical input is the time series of flow and sediment load being fed to the model for training. Hence, the accuracy and length of the measured time series data assume a special importance and if the same are available, then there is no reason that the present model would not perform equally well under varying turbidity conditions in different basins. Thus, the drawbacks associated with the WANN technique for SSL prediction are specific to numerical aspects, as identified above, and are not physical in nature.