IOP Conference Series: Earth and Environmental Science PAPER • OPEN ACCESS Neural Network Approaches to Modeling of Natural. Emergencies. Prediction of Lena River Spring High Waters To cite this article: G Struchkova et al 2021 IOP Conf. Ser.: Earth Environ. Sci. 666 032084 View the article online for updates and enhancements. This content was downloaded from IP address 170.106.202.126 on 25/09/2021 at 11:20 International science and technology conference "Earth science" IOP Publishing IOP Conf. Series: Earth and Environmental Science 666 (2021) 032084 doi:10.1088/1755-1315/666/3/032084 Neural Network Approaches to Modeling of Natural. Emergencies. Prediction of Lena River Spring High Waters G Struchkova1, M Lebedev1, V Timofeeva1, T Kapitonova1, A Gavrilieva1 1Larionov Institute of Physical-Technical Problems of the North Siberian Branch of the Russian Academy of Science, 677980 Yakutsk Oktyabrskaya St. 1, Russia E-mail:
[email protected] Abstract. Floods play a significant role in terms of damage and safety during construction and operation of crucial objects such as a bridge over the Lena river and underwater crossings of trunk pipelines in the North and the Arctic. A rapid rise of spring high water on the Lena river is due to accelerated melting of snow in a basin and a meridional flow direction of the river. If flood control measures are not taken, then severe economic and social consequences are inevitable, especially in places with complex infrastructure. As, for example, heavily populated cities, the strategically important objects, the underwater crossings of the trunk pipelines, bridges and power lines. This paper presents results of a study of a possibility of use of neural network algorithms to predict danger of the flood from the spring high waters on a section of the Lena river based on statistical archival data obtained over 70 years and an assessment of effectiveness of the neural network approach.