Artificial neural network ensemble modeling with exploratory factor analysis for streamflow forecasting

An artificial neural network (ANN) is a powerful data-driven modeling tool. The selection of the input variable is an important task in the development of an ANN model. However, at present in ANN modeling, the input variables are usually determined by trial and error methods. Further, the ANN modeler usually selects a single ‘good’ result, and accepts it as the final result without detailed explanation of the initial weight parameter. In this way, the ANN model cannot guarantee that the model will produce the optimal result for later predictions. In this study, the ANN ensemble model with exploratory factor analysis (EFA) was developed and applied to three stations in the Nakdong River, Korea for the 1-day ahead streamflow forecasting. EFA was used to select the input variables of the ANN model, and then the ensemble modeling was applied to estimate the performance of the ANN to remove the influence of initial weight parameters on the model results. In the result, the ANN ensemble model with the input variables proposed by EFA produced more accurate and reliable forecasts than other models with several combinations of input variables. Nash–Sutcliffe efficiency (NSE) results in the validation were 0.92, 0.95, and 0.97, respectively.

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