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

Тип документаJournal Article
Дата публикации2021-12-07
Название журналаWater (Switzerland)
ИздательMultidisciplinary Digital Publishing Institute (MDPI)
Квартиль по SCImagoQ1
Квартиль по Web of ScienceQ2
Импакт-фактор 20213.53
Water Science and Technology
Aquatic Science
Geography, Planning and Development
Краткое описание
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.
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1. Sarafanov M. и др. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River // Water. 2021. Т. 13. № 24. С. 3482.


DO - 10.3390/w13243482

UR -

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

T2 - Water

AU - Sarafanov, Mikhail

AU - Borisova, Yulia

AU - Maslyaev, Mikhail

AU - Revin, Ilia

AU - Maximov, Gleb

AU - Nikitin, Nikolay O.

PY - 2021

DA - 2021/12/07


SP - 3482

IS - 24

VL - 13

SN - 2073-4441

ER -

BibTex |


doi = {10.3390/w13243482},

url = {},

year = 2021,

month = {dec},

publisher = {{MDPI} {AG}},

volume = {13},

number = {24},

pages = {3482},

author = {Mikhail Sarafanov and Yulia Borisova and Mikhail Maslyaev and Ilia Revin 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}


Sarafanov, Mikhail, et al. “Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River.” Water, vol. 13, no. 24, Dec. 2021, p. 3482. Crossref,