Using the Methods of Neural Network Learning for Peak Water Level Prediction: A Case Study for the Rivers in the Dvina-Pechora Basin

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-06-27 DOI:10.3103/s1068373924040095

A. E. Sumachev, L. S. Banshchikova, S. A. Griga

引用次数: 0

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.

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使用神经网络学习方法预测水位峰值：德维纳-佩乔拉盆地河流案例研究

摘要本文以苏霍纳河、北德维纳河和佩乔拉河为例，研究了预测春季流冰期高峰水位的神经网络方法的实施情况。根据俄罗斯水文中心推荐的标准，所有考虑采用的神经网络方法都显示出很高的效率，并且在预测技能方面超过了回归依赖法。在使用人工神经网络训练方法时，预测的标准误差比回归依赖法减少了约 10-20%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.