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.
In recent decades there has been a trend towards an increase in the number of dangerous hydrological events, especially floods. In order to protect citizens and solve economic problems, it is important to develop and actively introduce into operational practice methods of hydrological forecasting, as well as to build more modern and convenient interfaces of interaction between hydrometeorological services, municipal authorities and citizens. This work discusses a compact automated short-term hydrological forecasting system that uses open-source conceptual models HBV, SimHYD and GR4J as its core. The system is connected to data streams on the observed temperatures and precipitation in the watershed basin, as well as the predicted values of these parameters (in a current implementation, the WRF model with a forecast for 84 hours is used). Also, for operational calibration in daily mode, the system can assimilate (if available) data on observed water levels. Testing of the system is carried out on the example of Tikhvin city (the Tikhvinka river), which in recent years has been characterized by frequent flooding.
The article presents the results of the development of a model for calculating levels at one gauging station using the levels at another. To link the levels at two gauging stations, the data on levels, temperature and precipitation were used. The use of machine learning methods to solve the problem of predicting water levels made it possible to achieve an accuracy of about 6 cm. At the same time, traditional statistical models (linear regression, polynomial regression) have 14-16 cm error.
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