Применение методов обучения искусственных нейронных сетей при прогнозировании высших уровней воды на примере рек Двинско-Печорского бассейнового округа

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Применение методов обучения искусственных нейронных сетей при прогнозировании высших уровней воды на примере рек Двинско-Печорского бассейнового округа. / Сумачев, Александр Эдуардович; Банщикова, Л.С.; Грига, Семен Алексеевич.

In: Метеорология и гидрология, Vol. 2024, No. 4, 01.04.2024, p. 104-115.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{7d24145bcd6145d59ac84ed85eaa8ec3,

title = "Применение методов обучения искусственных нейронных сетей при прогнозировании высших уровней воды на примере рек Двинско-Печорского бассейнового округа",

abstract = "The paper examines the implementation of neural network methods for predicting peak water levels during the period of spring ice drift by the example of the Sukhona, Northern Dvina, and Pechora rivers. All considered neural network methods have shown high efficiency according to the criteria recommended by the Hydrometcenter of Russia and surpassed regression dependencies in the skill of forecasts. When using the method of training artificial neural networks, the standard error of prediction is reduced by approximately 10-20% as compared with regression dependencies.",

author = "Сумачев, {Александр Эдуардович} and Л.С. Банщикова and Грига, {Семен Алексеевич}",

year = "2024",

month = apr,

day = "1",

doi = "10.52002/0130-2906-2024-4-104-115",

language = "русский",

volume = "2024",

pages = "104--115",

journal = "Meteorologiya i Gidrologiya",

issn = "0130-2906",

publisher = "Научно-исследовательский центр космической гидрометеорологии Планета",

number = "4",

}

RIS

TY - JOUR

T1 - Применение методов обучения искусственных нейронных сетей при прогнозировании высших уровней воды на примере рек Двинско-Печорского бассейнового округа

AU - Сумачев, Александр Эдуардович

AU - Банщикова, Л.С.

AU - Грига, Семен Алексеевич

PY - 2024/4/1

Y1 - 2024/4/1

N2 - The paper examines the implementation of neural network methods for predicting peak water levels during the period of spring ice drift by the example of the Sukhona, Northern Dvina, and Pechora rivers. All considered neural network methods have shown high efficiency according to the criteria recommended by the Hydrometcenter of Russia and surpassed regression dependencies in the skill of forecasts. When using the method of training artificial neural networks, the standard error of prediction is reduced by approximately 10-20% as compared with regression dependencies.

AB - The paper examines the implementation of neural network methods for predicting peak water levels during the period of spring ice drift by the example of the Sukhona, Northern Dvina, and Pechora rivers. All considered neural network methods have shown high efficiency according to the criteria recommended by the Hydrometcenter of Russia and surpassed regression dependencies in the skill of forecasts. When using the method of training artificial neural networks, the standard error of prediction is reduced by approximately 10-20% as compared with regression dependencies.

UR - https://www.mendeley.com/catalogue/35e02642-b166-3e8c-b8a7-a896e20dabe7/

U2 - 10.52002/0130-2906-2024-4-104-115

DO - 10.52002/0130-2906-2024-4-104-115

M3 - статья

VL - 2024

SP - 104

EP - 115

JO - Meteorologiya i Gidrologiya

JF - Meteorologiya i Gidrologiya

SN - 0130-2906

IS - 4

ER -

ID: 119909936