Uncertainty decomposition and reduction in river flood forecasting: Belgian case study

2014 ◽  
Vol 8 (3) ◽  
pp. 263-275 ◽  
Author(s):  
N. Van Steenbergen ◽  
P. Willems
Keyword(s):  
Flood Forecasting ◽  
River Flood

scholarly journals Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3482
Author(s):  
Mikhail Sarafanov ◽  
Yulia Borisova ◽  
Mikhail Maslyaev ◽  
Ilia Revin ◽  
Gleb Maximov ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Water ◽  
Flood Forecasting ◽  
Experimental Comparison ◽  
Efficiency Coefficient ◽  
Short Term ◽  
Lena River ◽  
River Flood ◽  
Automated Machine Learning

The paper presents a hybrid approach for short-term river flood forecasting. It is based on multi-modal data fusion from different sources (weather stations, water height sensors, remote sensing data). To improve the forecasting efficiency, the machine learning methods and the Snowmelt-Runoff physical model are combined in a composite modeling pipeline using automated machine learning techniques. The novelty of the study is based on the application of automated machine learning to identify the individual blocks of a composite pipeline without involving an expert. It makes it possible to adapt the approach to various river basins and different types of floods. Lena River basin was used as a case study since its modeling during spring high water is complicated by the high probability of ice-jam flooding events. Experimental comparison with the existing methods confirms that the proposed approach reduces the error at each analyzed level gauging station. The value of Nash–Sutcliffe model efficiency coefficient for the ten stations chosen for comparison is 0.80. The other approaches based on statistical and physical models could not surpass the threshold of 0.74. Validation for a high-water period also confirms that a composite pipeline designed using automated machine learning is much more efficient than stand-alone models.

scholarly journals Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales)

2013 ◽  
Vol 17 (1) ◽  
pp. 177-185 ◽  
Author(s):  
D. Leedal ◽  
A. H. Weerts ◽  
P. J. Smith ◽  
K. J. Beven
Keyword(s):  
Warning System ◽  
Open Shell ◽  
Flood Forecasting ◽  
Mechanistic Modelling ◽  
Forecasting Models ◽  
Forecasting System ◽  
Environment Agency ◽  
The Uk ◽  
Modelling Approach

Abstract. The Delft Flood Early Warning System provides a versatile framework for real-time flood forecasting. The UK Environment Agency has adopted the Delft framework to deliver its National Flood Forecasting System. The Delft system incorporates new flood forecasting models very easily using an "open shell" framework. This paper describes how we added the data-based mechanistic modelling approach to the model inventory and presents a case study for the Eden catchment (Cumbria, UK).

scholarly journals Optimal floodgate operation for river flood management: The case study of Padova (Italy)

2020 ◽  
Vol 30 ◽  
pp. 100702 ◽  
Author(s):  
Riccardo A. Mel ◽  
Daniele P. Viero ◽  
Luca Carniello ◽  
Luigi D’Alpaos
Keyword(s):  
Flood Management ◽  
River Flood

River flood forecasting with a neural network model

1999 ◽  
Vol 35 (4) ◽  
pp. 1191-1197 ◽  
Author(s):  
Marina Campolo ◽  
Paolo Andreussi ◽  
Alfredo Soldati
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Flood Forecasting ◽  
River Flood

Real-Time Flood Forecasting System: Case Study of Hsia-Yun Watershed, Taiwan

2016 ◽  
Vol 21 (4) ◽  
pp. 05015031 ◽  
Author(s):  
Jen-Kuo Huang ◽  
Ya-Hsin Chan ◽  
Kwan Tun Lee
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Forecasting System
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