Journal
JOURNAL OF HYDROLOGY
Volume 230, Issue 3-4, Pages 244-257Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/S0022-1694(00)00214-6
Keywords
real-time forecasting; reservoir inflow; artificial neural networks; stopped training approach
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In this paper, an early stopped training approach (STA) is introduced to train multi-layer feed-forward neural networks (FNN) for real-time reservoir inflow forecasting. The proposed method takes advantage of both Levenberg-Marquardt Backpropagation (LMBP) and cross-validation technique to avoid underfitting or overfitting on FNN training and enhances generalization performance. The methodology is assessed using multivariate hydrological time series from Chute-du-Diable hydrosystem in northern Quebec (Canada). The performance of the model is compared to benchmarks from a statistical model and an operational conceptual model. Since the ultimate goal concerns the real-time forecast accuracy, overall the results show that the proposed method is effective for improving prediction accuracy. Moreover it offers an alternative when dynamic adaptive forecasting is desired. (C) 2000 Elsevier Science B.V. All rights reserved.
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