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

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

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...

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Bibliographic Details
Main Authors: Yanlai Zhou, Shenglian Guo, Chong-Yu Xu, Fi-John Chang, Jiabo Yin
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Water
Subjects:
probabilistic forecast
unscented kalman filter
artificial neural networks
three gorges reservoir
Online Access:https://www.mdpi.com/2073-4441/12/2/578

Internet

https://www.mdpi.com/2073-4441/12/2/578

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