Two Semidistributed ANN-Based Models for Estimation of Suspended Sediment Load

The sediment load transported in a river is the most complex hydrological phenomenon due to a large number of obscure parameters and the existence of both spatial variability of the basin characteristics and temporal climatic patterns. In this paper two artificial neural network (ANN) models were developed for semidistributed modeling of the suspended sediment load process of the Eel River watershed located in California. The first model was an integrated ANN model trained by the data of multiple stations inside the watershed. In the second model, a geomorphology-based ANN model, space-dependent geomorphologic parameters of the subbasins, extracted by geographic information system tools, accompanied by time-dependent meteorological data, were imposed on the network. In both models, three-layer perceptron neural networks were trained considering various combinations of input and hidden layers’ neurons, and the optimum architectures of the models were selected according to the computed evaluation criteria. Furthermore, the ability of the models for spatiotemporal modeling of the process was examined through the cross-validation technique for a station. The obtained results demonstrate that although the predicted sediment load time series by both models are in satisfactory agreement with the observed data, the geomorphological ANN model produces better performance than an integrated model because it employs spatially variable factors of the subbasins as the model’s inputs. Therefore, the model can operate as a nonlinear time-space regression tool rather than a fully lumped model.