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...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-02-01
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Series: | Water |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4441/12/2/578 |