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

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

▼ Show Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Share and Cite

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

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI