Integrated artificial neural network for spatiotemporal modeling of rainfall-runoff-sediment processes.

Abstract This article tries to develop an integrated artificial neural network (ANN) model for spatial and temporal forecasting of daily suspended sediment discharge at multiple gauging stations in Eel River watershed in northwest California. Complexity of runoff–sediment process and its variability in space and time and also lack of historical sediment data cause difficulties in spatiotemporal modeling of this process. Initially, and for comparison purpose, six single-station ANN models, which are customary in modeling sediment yield, were developed. Then an integrated ANN model for modeling multiple stations was proposed and its spatiotemporal modeling ability was examined through a cross-validation technique for a station. In this way, different multilayer perceptron neural networks were trained using Levenberg–Marquardt algorithm to estimate daily values of suspended sediment discharge. Various combinations of input and hidden layers' neurons were applied and the optimum architectures of the models we...

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