River system sediment flow modeling using artificial neural networks

Sediment leads to problems with navigation, agricultural productivity, and water pollution. The study of sediment flow in river reaches, which is a non-linear and complex process, is, thus, essential to addressing these issues. The application of artificial neural networks (ANN) to such problems needs to be investigated. For unsteady flow in a river system, river reach storage is an important variable that needs to be considered in data-driven models. However, previous research on sediment modeling did not involve the explicit use of storage variables in such models as is investigated in the current study. In the current study, storage variables have been explicitly (Model 2) used to predict the output state of the system at time ‘t + 1’ from the input state at time ‘t’ using ANNs. Sediment discharge at six gaging stations on the Mississippi River system, USA, has been considered as the state variable. The model has been compared with a model considering implicit variation of the storage parameter in the river system (Model 1). Dynamic ANNs are used for time-series datasets, which are more suitable for incorporating the sequential information within the dataset. Focussed gamma memory neural networks have been used in the current study. The numbers of hidden layers and hidden nodes, activation function, and learning rate have been varied step by step to obtain the optimal ANN configurations. The best selected input–output variables are those used in Model 2 as it performed slightly better than the other model in terms of Nash–Sutcliffe efficiency coefficient (CE) values. Model performance evaluated using normalized root mean square error (NRMSE) and CE shows satisfactory results. NRMSE was < 10% for all the outputs except for the Venedy and Murphysboro locations and CE values for sediment loads were > 0.45 for all locations except Murphysboro indicating acceptable performance by both the models. The models proved highly efficient (CE > 0.80, i.e., very good predictions) for predicting sediment discharge at locations along the main river channel with acceptable accuracy (CE > 0.45) for other locations and the storage change for the river system. These models can be used for real-time forecasting and management of sediment-related problems.