Open Access
Water (Switzerland), volume 13, issue 24, pages 3482
Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River
Publication type: Journal Article
Publication date: 2021-12-07
Biochemistry
Water Science and Technology
Aquatic Science
Geography, Planning and Development
Abstract
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.
Citations by journals
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1
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Computers and Geosciences
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1 publication, 14.29%
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1 publication, 14.29%
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Journal of Marine Science and Engineering
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Knowledge-Based Systems
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1 publication, 14.29%
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Lecture Notes in Networks and Systems
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1 publication, 14.29%
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Remote Sensing
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1 publication, 14.29%
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1
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Citations by publishers
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1
2
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Elsevier
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Elsevier
2 publications, 28.57%
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
2 publications, 28.57%
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Copernicus
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Copernicus
1 publication, 14.29%
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Springer Nature
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Springer Nature
1 publication, 14.29%
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Wiley
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Wiley
1 publication, 14.29%
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1
2
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- We do not take into account publications that without a DOI.
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Sarafanov M. et al. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River // Water (Switzerland). 2021. Vol. 13. No. 24. p. 3482.
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Sarafanov M., Borisova Y., Maslyaev M., Revin I., Maximov G., Nikitin N. O. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River // Water (Switzerland). 2021. Vol. 13. No. 24. p. 3482.
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TY - JOUR
DO - 10.3390/w13243482
UR - https://doi.org/10.3390%2Fw13243482
TI - Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River
T2 - Water (Switzerland)
AU - Borisova, Yulia
AU - Maslyaev, Mikhail
AU - Revin, Ilia
AU - Sarafanov, Mikhail
AU - Maximov, Gleb
AU - Nikitin, Nikolay O
PY - 2021
DA - 2021/12/07 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 3482
IS - 24
VL - 13
SN - 2073-4441
ER -
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@article{2021_Sarafanov,
author = {Yulia Borisova and Mikhail Maslyaev and Ilia Revin and Mikhail Sarafanov and Gleb Maximov and Nikolay O Nikitin},
title = {Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River},
journal = {Water (Switzerland)},
year = {2021},
volume = {13},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {dec},
url = {https://doi.org/10.3390%2Fw13243482},
number = {24},
pages = {3482},
doi = {10.3390/w13243482}
}
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MLA
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Sarafanov, Mikhail, et al. “Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River.” Water (Switzerland), vol. 13, no. 24, Dec. 2021, p. 3482. https://doi.org/10.3390%2Fw13243482.