E3S Web Conf.
Volume 163, 2020IV Vinogradov Conference “Hydrology: from Learning to Worldview” in Memory of Outstanding Russian Hydrologist Yury Vinogradov
|Number of page(s)||6|
|Section||Mathematical Modeling in Hydrology: Problems, Achievements, Practical Application|
|Published online||17 April 2020|
Modeling water level using downstream river water level observations and machine learning methods
State Hydrological Institute, 23 2-nd line, Vasilievsky Island, 199004, St. Petersburg, Russia
2 ITMO University, 49 Kronversky prospekt, 197101, St. Petersburg, Russia
3 Saint Petersburg State University, 7/9 Universitetskaya nab, 199034, St. Petersburg, Russia
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.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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