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Article

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

National Center for Cognitive Research, ITMO University, 49 Kronverksky Pr., 197101 Petersburg, Russia
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Academic Editors: Zheng Duan and Babak Mohammadi
Water 2021, 13(24), 3482; https://doi.org/10.3390/w13243482
Received: 9 November 2021 / Revised: 29 November 2021 / Accepted: 3 December 2021 / Published: 7 December 2021
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. View Full-Text
Keywords: flood forecasting; automated machine learning; composite artificial intelligence flood forecasting; automated machine learning; composite artificial intelligence
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MDPI and ACS Style

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 2021, 13, 3482. https://doi.org/10.3390/w13243482

AMA Style

Sarafanov M, Borisova Y, Maslyaev M, Revin I, Maximov G, Nikitin NO. Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River. Water. 2021; 13(24):3482. https://doi.org/10.3390/w13243482

Chicago/Turabian Style

Sarafanov, Mikhail, Yulia Borisova, Mikhail Maslyaev, Ilia Revin, Gleb Maximov, and Nikolay O. Nikitin. 2021. "Short-Term River Flood Forecasting Using Composite Models and Automated Machine Learning: The Case Study of Lena River" Water 13, no. 24: 3482. https://doi.org/10.3390/w13243482

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