Influence of digital technologies on the quality of life by modelling the risk of flooding of territories by flood waters

Grebnev, Ya V.; Moskalev, A K; Shagidulina, D I

doi:10.1088/1742-6596/2094/2/022061

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…Water resources are vital to the planet, and understanding the movement of water is critical to effectively managing water systems, predicting floods, optimizing energy processes, and developing sustainable solutions. The development of a cyber-physical model of water movement is a promising area of research that combines physical modeling and computational methods [1][2].…”

Section: Introductionmentioning

confidence: 99%

Development of a cyber-physical model for the movement of surface and ground waters during a flood in Arctic conditions

Grebnev,

Moskalev,

Petrunina

et al. 2024

E3S Web Conf.

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The development of physical models that take into account the combined influence of the dynamics of surface and ground waters is an important task for solving the problem of flooding of settlements during floods. These models allow improving the accuracy of modeling considering more complex physical processes, adapting to changing climatic conditions and integrating with other control systems. This paper explores the use of cyber-physical methods for modeling the flood zones of settlements during floods. The MODFLOW software product was used to simulate the flood zones of settlements. The developed model makes it possible to analyze hydrological processes, including groundwater dynamics and interaction with surface water bodies. Neural network models have also been developed and tested that use geographic, meteorological, hydrological, and structure and infrastructure data to predict the boundaries of flood zones.

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Section: Introductionmentioning

confidence: 99%

Development of a cyber-physical model for the movement of surface and ground waters during a flood in Arctic conditions

Grebnev,

Moskalev,

Petrunina

et al. 2024

E3S Web Conf.

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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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“…Among them are both a wide range of sources of emergency situations of a natural, technogenic and biological and social nature, as well as the prevailing features of the socio-economic development of the region. Currently, there is a significant amount of scientific research and applied development to ensure the problems of industrial safety [1][2][3][4][5][6][7]. The issues of assessing the risk of the consequences of hypothetical accidents at large man-made objects are becoming increasingly important.…”

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 digital technologies on the quality of life by modelling the risk of flooding of territories by flood waters

Cited by 3 publications

References 1 publication

Development of a cyber-physical model for the movement of surface and ground waters during a flood in Arctic conditions

Development of a cyber-physical model for the movement of surface and ground waters during a flood in Arctic conditions

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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