Integrated Artificial Neural Network for Spatiotemporal Modeling of Rainfall–Runoff–Sediment Processes

Abstract

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 were selected according to the obtained evaluation criteria in the terms of Nash–Sutcliffe efficiency coefficient, root mean-squared error, and ratio of absolute error of peak flow. To improve the model, input data were classified into two clusters by k-means clustering scheme. Afterwards, clustered data were used as inputs for two integrated models and their performances were evaluated. The proposed integrated ANN model shows reasonable performance in spatiotemporal modeling both before and after clustering. Nevertheless, clustering decreases the complexity of the model.