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Open AccessArticle

Identifying the Sensitivity of Ensemble Streamflow Prediction by Artificial Intelligence

1
Institute of Hydrology and Water Resources, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2
Ocean College, Zhejiang University, Hangzhou 316021, China
3
Department of Civil and Environmental Engineering, Pennsylvania State University, State College, PA 16801, USA
*
Author to whom correspondence should be addressed.
Water 2018, 10(10), 1341; https://doi.org/10.3390/w10101341
Received: 30 August 2018 / Revised: 23 September 2018 / Accepted: 26 September 2018 / Published: 27 September 2018
(This article belongs to the Special Issue Flood Forecasting Using Machine Learning Methods)
Sustainable water resources management is facing a rigorous challenge due to global climate change. Nowadays, improving streamflow predictions based on uneven precipitation is an important task. The main purpose of this study is to integrate the ensemble technique concept into artificial neural networks for reducing model uncertainty in hourly streamflow predictions. The ensemble streamflow predictions are built following two steps: (1) Generating the ensemble members through disturbance of initial weights, data resampling, and alteration of model structure; (2) consolidating the model outputs through the arithmetic average, stacking, and Bayesian model average. This study investigates various ensemble strategies on two study sites, where the watershed size and hydrological conditions are different. The results help to realize whether the ensemble methods are sensitive to hydrological or physiographical conditions. Additionally, the applicability and availability of the ensemble strategies can be easily evaluated in this study. Among various ensemble strategies, the best ESP is produced by the combination of boosting (data resampling) and Bayesian model average. The results demonstrate that the ensemble neural networks greatly improved the accuracy of streamflow predictions as compared to a single neural network, and the improvement made by the ensemble neural network is about 19–37% and 20–30% in Longquan Creek and Jinhua River watersheds, respectively, for 1–3 h ahead streamflow prediction. Moreover, the results obtained from different ensemble strategies are quite consistent in both watersheds, indicating that the ensemble strategies are insensitive to hydrological and physiographical factors. Finally, the output intervals of ensemble streamflow prediction may also reflect the possible peak flow, which is valuable information for flood prevention. View Full-Text
Keywords: ensemble technique; artificial neural networks; uncertainty; streamflow predictions; sensitivity ensemble technique; artificial neural networks; uncertainty; streamflow predictions; sensitivity
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Chiang, Y.-M.; Hao, R.-N.; Zhang, J.-Q.; Lin, Y.-T.; Tsai, W.-P. Identifying the Sensitivity of Ensemble Streamflow Prediction by Artificial Intelligence. Water 2018, 10, 1341.

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