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Open AccessEditor’s ChoiceArticle

Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

1
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
2
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, N-0316 Oslo, Norway
3
Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, Taiwan
*
Authors to whom correspondence should be addressed.
Water 2020, 12(2), 578; https://doi.org/10.3390/w12020578
Received: 7 January 2020 / Revised: 13 February 2020 / Accepted: 17 February 2020 / Published: 20 February 2020
(This article belongs to the Special Issue Advances in Hydrologic Forecasts and Water Resources Management )
It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved. View Full-Text
Keywords: probabilistic forecast; Unscented Kalman Filter; artificial neural networks; Three Gorges Reservoir probabilistic forecast; Unscented Kalman Filter; artificial neural networks; Three Gorges Reservoir
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Zhou, Y.; Guo, S.; Xu, C.-Y.; Chang, F.-J.; Yin, J. Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network. Water 2020, 12, 578.

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