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

• Published in: Water
• Main Authors: Mikhail Sarafanov, Yulia Borisova, Mikhail Maslyaev, Ilia Revin, Gleb Maximov, Nikolay O. Nikitin
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: flood forecasting; automated machine learning; composite artificial intelligence; Hydraulic engineering; TC1-978; Water supply for domestic and industrial purposes; TD201-500; Nash; Sutcliffe; lena river
• Online Access: https://doi.org/10.3390/w13243482; https://doaj.org/article/ec54427fd794415b8d8c8ef02988913e

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.