# Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers
###### Abstract
[49] studied two different hydro-ecological rivers with different land-cover characteristics and concluded that the WANN models were successful in the prediction of SSL. Their assessment encompassed a river draining a small basin (142 km\({}^{2}\)) as well as a river with a large basin (\(\approx\)10,000 km\({}^{2}\)). It was also suggested that the good quality of results obtained using WANN for SSL modelling merited its usage to model other hydro-environmental (groundwater, precipitation, etc.) processes. Despite the adequate efficiency of the coupled WANN model approach, some drawbacks can be associated with WANN 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 to train the ANN models is the most important challenge to avoid errors and a complex dataset. Investigating the number of the appropriate input variables and the appropriate data length for model development is one of the particular aims of the current study, which aims to develop a precise model for predicting SSL. In view of the aforementioned facts, the present study was undertaken to investigate and compare the performance of several soft computing techniques (LLR, ANN and WANN models) with a conventional modelling technique (SRC) for the estimation of daily SSL at the Pirna and Magdeburg Stations of Elbe River, Germany. The objectives of the current study are: (a) to evaluate the best technique for the prediction of daily SSL by employing different models at different sites; (b) to evaluate the selection of appropriate input variables and data length for a training dataset using the Gamma test and M-test; (c) to analyze the best-selected input parameters and validate the prediction performance based on sensitivity analysis. The novelty of this research lies in the usage of the Hill-climbing model identification technique, i.e., Gamma test and the M test, for the prediction of daily SSL in rivers. As the hydrological processes are highly complex, dynamic, and non-linear, finding the best-input combination is important. In the literature, this is performed through a trial-and-error approach, which is laborious. In order to overcome this problem, a novel mathematical tool, the Gamma test (GT), which is a non-parametric test, is adopted for the selection of the most suitable input variables, which would lead to a reliable and smooth model. Furthermore, an important issue in data-driven models is the determination of a suitable data length for the model development, which is neither under-fitted nor over-fitted. In general, the literature suggested reserving 70% to 90% of the dataset to train a model. However, in order to find the optimal training data length, we used the M-test for model development. The M-test shows the relationship between datapoints and Gamma statistic (\(\Gamma\)) values, which can be used to determine the data length needed to obtain a uniform function convergence.