Open Access
Open access
volume 163 pages 1009

Modeling water level using downstream river water level observations and machine learning methods

Publication typeJournal Article
Publication date2020-04-17
SJR0.205
CiteScore1.1
Impact factor
ISSN22671242, 25550403
Pulmonary and Respiratory Medicine
Pediatrics, Perinatology, and Child Health
Abstract

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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Kazakov E. et al. Modeling water level using downstream river water level observations and machine learning methods // E3S Web of Conferences. 2020. Vol. 163. p. 1009.
GOST all authors (up to 50) Copy
Sarafanov M., Kazakov E., Borisova Y. Y. Modeling water level using downstream river water level observations and machine learning methods // E3S Web of Conferences. 2020. Vol. 163. p. 1009.
RIS |
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RIS Copy
TY - JOUR
DO - 10.1051/e3sconf/202016301009
UR - https://doi.org/10.1051/e3sconf/202016301009
TI - Modeling water level using downstream river water level observations and machine learning methods
T2 - E3S Web of Conferences
AU - Sarafanov, Mikhail
AU - Kazakov, Eduard
AU - Borisova, Yulia Y.
PY - 2020
DA - 2020/04/17
PB - EDP Sciences
SP - 1009
VL - 163
SN - 2267-1242
SN - 2555-0403
ER -
BibTex
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BibTex (up to 50 authors) Copy
@article{2020_Kazakov,
author = {Mikhail Sarafanov and Eduard Kazakov and Yulia Y. Borisova},
title = {Modeling water level using downstream river water level observations and machine learning methods},
journal = {E3S Web of Conferences},
year = {2020},
volume = {163},
publisher = {EDP Sciences},
month = {apr},
url = {https://doi.org/10.1051/e3sconf/202016301009},
pages = {1009},
doi = {10.1051/e3sconf/202016301009}
}