Influence of hydroelectric power station on flood situation and modeling of hydrological characteristics

Grebnev, Yaroslav; Moskalev, A K; Vershkov, Anatoly

doi:10.1051/e3sconf/202022102002

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…The problem of creating an effective risk management system for technogenic systems was considered in the works of the authors (Medvedeva & Solovieva, 2016;Mikheeva & Ayusheeva, 2014;https://doi.org/10.15405/epsbs.2021.09.02.294 Corresponding Author: Y. V. Grebnev Selection and peer-review under responsibility of the Organizing Committee of the conference eISSN: 2357-1330 2643 Moskvichev & Nicheporchuk, 2018;Moskvichev et al, 2017;Potravny & Ngo, 2018). The significance of the issues of forecasting emergency situations was raised in the works of a number of researchers such as Korovin (1969), Grebnev et al (2019), Grebnev and Moskalev (2020), Dorrer (2008), Volchek (2010), Chuchueva (2010). The problems of regulating the use of polluted lands and water bodies were considered in the works of the authors (Cappuyns, 2013;Carlon et al, 2007;Fentiman & Zabbey, 2015).…”

Section: Problem Statementmentioning

confidence: 99%

Modeling The Assessment Of Technogenic Risks Impact At Arctic Enterprises

Grebnev¹

2021

European Proceedings of Social and Behavioural Sciences

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Section: Problem Statementmentioning

confidence: 99%

Modeling The Assessment Of Technogenic Risks Impact At Arctic Enterprises

Grebnev¹

2021

European Proceedings of Social and Behavioural Sciences

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“…The introduction of big data technologies and neural network forecasting make it possible to conduct better predictive analysis with different forecasting horizons [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Development of a model for forecasting the inflow to the site of a hydroelectric power plant using neural models for information processing

Grebnev,

Moskalev,

Petrunina

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Information technologies have entered all spheres of human life. Now they are an integral part of the modern world. These changes also affected the anti-crisis management system. The development of end-to-end technologies and the use of artificial intelligence allows for a better assessment and forecasting of risks. The methods used for forecasting the inflow to the site of a hydroelectric power station were analyzed in this paper. The data necessary to obtain a forecast were reviewed. An approach related to forecasting the inflow to the site of a hydroelectric power station using neural network forecasting methods is demonstrated. Using the python programming language, a neural network model was developed to predict the inflow to the hydroelectric station site. A recurrent network with a long short-term memory LSTM using 64 neurons was chosen as the network architecture. The data array for forecasting was obtained from the data of operational monitoring of the flood situation and archival data on flooding of the territory over the past 13 years, taken from the database of the Yenisei Basin Water Administration of the Federal Agency for Water Resources and the Central Siberian UGMS. The input parameters were selected empirically. When comparing real data on flow with the predicted ones, the error was 17%.

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“…One of the effective ways to prevent the risk of emergencies at hydraulic structures is the organization of operational monitoring and modeling of processes and risk assessment. One of the effective modeling methods that have recently become widespread is big data analysis and neural network forecasting [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Assessment of natural and technogenic hazard at large hydraulic structures in the northern and arctic territories

Grebnev,

Moskalev,

Petrunina

2023

E3S Web Conf.

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Hydroelectric power plants are an important source of electricity in the world, but at the same time they can face various emergency situations that can lead to significant economic and environmental consequences. Recently, neural networks have become an increasingly popular tool for modeling and predicting emergency situations at hydroelectric power plants. This article discusses approaches to assessing the consequences of possible accidents at northern hydraulic structures. To develop scenarios and models, the Toxy + risk software and analytical complex and recurrent neural networks developed in the python programming language were used in the work. Various scenarios for the development of emergency situations have been developed and an assessment of the risk of their occurrence has been made.

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Influence of hydroelectric power station on flood situation and modeling of hydrological characteristics

Cited by 5 publications

References 4 publications

Modeling The Assessment Of Technogenic Risks Impact At Arctic Enterprises

Modeling The Assessment Of Technogenic Risks Impact At Arctic Enterprises

Development of a model for forecasting the inflow to the site of a hydroelectric power plant using neural models for information processing

Assessment of natural and technogenic hazard at large hydraulic structures in the northern and arctic territories

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