Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 11
Abstract
Artificial neural networks (ANNs) have proved to be an efficient alternative to traditional methods for hydrological modeling. One of the most important steps in the ANN development is the determination of significant input variables. This study proposes a new method based on the copula-entropy (CE) theory to identify the inputs of an ANN model. The CE theory permits to calculate mutual information (MI) and partial mutual information (PMI), which characterizes the dependence between potential model input and output variables directly instead of calculating the marginal and joint probability distributions. Two tests were carried out for verifying the accuracy and performance of the CE method. The CE theory-based input determination methodology was applied to identify suitable inputs for a flood forecasting model for a real-world case study involving the three gorges reservoir (TGR) in China. Test results of application of the flood forecasting model to the upper Yangtze River indicates that the proposed method appropriately identifies inputs for the ANN with the smallest root-mean-square error (RMSE) for training, testing, and validation data.
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Acknowledgments
The project was financially supported by the National Natural Science Foundation of China (NSFC Grant 51309104, 51239004, 51190094), Fundamental Research Funds for the Central Universities (2013QN113) and Natural Science Foundation of Hubei Province (No. 2013CFB184).
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Journal of Hydrologic Engineering
Volume 19 • Issue 11 • November 2014
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© 2014 American Society of Civil Engineers.
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Received: Feb 19, 2013
Accepted: Oct 24, 2013
Published online: Oct 26, 2013
Published in print: Nov 1, 2014
Discussion open until: Dec 8, 2014
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