Interactive map of environmental pollution of the surface waters in Lviv region

Sohor, Andrii; Brydun, A. M.; Yarema, N.; Lozynskyi, V.; Sohor, Markiian

doi:10.3997/2214-4609.20205751

Cited by 2 publications

(3 citation statements)

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“…Neural networks have a number of advantages over traditional methods of environmental monitoring [30,31]: -High accuracy: neural networks can be trained on large data sets, which allows them to achieve high accuracy in classification and forecasting tasks; -speed: neural networks can process large amounts of data quickly and efficiently; -flexibility: neural networks can be adapted to various environmental monitoring tasks. However, there are also some challenges associated with using neural networks for environmental monitoring [12][13][14][15]: -Data requirements: neural networks require large data sets to train.…”

Section: Literature Reviewmentioning

confidence: 99%

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Neural Networks for Environmental Monitoring: A Web Application for Image Recognition

Bernatska,

Dzhumelia,

Typilo

et al. 2024

Advances in Transdisciplinary Engineering

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Environmental monitoring is increasingly reliant on image recognition through neural networks, automating the identification and classification of environmental threats. This automation optimizes resource allocation, enhances monitoring accuracy, and empowers researchers to address climate-related challenges. In response to this, we present a web application built using React.js and TensorFlow.js, which enables real-time image recognition. This application facilitates tasks ranging from climate analysis to disaster response and wildlife conservation, offering valuable insights into environmental trends. Moreover, it engages the public, promotes awareness, and contributes to conservation efforts, making it a versatile and cost-effective tool for environmental monitoring. The application leverages advancements in image recognition technology, availability of datasets, open-source frameworks, and cloud computing infrastructure. It supports a wide range of applications, promotes global collaboration, and ensures data security. While challenges exist, the potential of neural networks in environmental monitoring is promising. The web application combines the power of React.js and artificial neural networks, enhancing environmental monitoring efficacy and fostering sustainable practices on a global scale. Ongoing research and standardization efforts are essential for refining and expanding this approach.

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Section: Literature Reviewmentioning

confidence: 99%

“…A web application for a neural network is software that allows users to interact with a neural network through a web browser [16][17]. Web applications can be used for various purposes, such as training, testing, and utilizing neural networks [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Networks for Environmental Monitoring: A Web Application for Image Recognition

Bernatska,

Dzhumelia,

Typilo

et al. 2024

Advances in Transdisciplinary Engineering

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“…However, at the same time, the activity of mining and chemical enterprises is a determining factor of technogenesis, which significantly complicates the ecological situation in local territories with a change in the forms of the relief, hydrological and biogeochemical regimes of the territories, which is caused by the accumulation of a significant amount of production waste on the earth's surface (Pohrebennyk et al, 2017). Thus, various environmental problems arise, which are connected, first of all, with the pollution of various natural components of the environment -soils, waters, vegetation, and therefore the deterioration of the health indicators of the local population (Pohrebennyk et al, 2017;Sohor et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Web-Based Information and Analytical Monitoring System Tools – Online Visualization and Analysis of Surface Water Quality of Mining and Chemical Enterprises

Bernatska¹,

Dzhumelia²,

Dyakiv³

et al. 2023

Ecol. Eng. Environ. Technol.

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An analysis of the quality of surface water of State Enterprise Rozdil Mining and Chemical Enterprise 'Sirka' was carried out. It was established that in order to ensure ecological balance in the zone of influence of this enterprise, it is necessary to conduct regular monitoring observations, maintenance, supervision and control over the condition of hydraulic structures, elimination of sources of pollution. The obtained research results indicate that there is a need to create an information and analytical monitoring system in order to effectively store, process, and analyze the data based on the principles of comprehensive environmental monitoring for the collection, storage, and processing of data on pollution of various elements of the environment, which will provide forecasting of environmental changes in the territory of the mining and chemical enterprise. On the basis of the obtained research results, a web application was created based on an interactive map of water sampling points, visualization of the obtained results of hydrochemical monitoring of Rozdil Lakes, and a forecast of the state of the water environment.

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Interactive map of environmental pollution of the surface waters in Lviv region

Cited by 2 publications

References 0 publications

Neural Networks for Environmental Monitoring: A Web Application for Image Recognition

Neural Networks for Environmental Monitoring: A Web Application for Image Recognition

Web-Based Information and Analytical Monitoring System Tools – Online Visualization and Analysis of Surface Water Quality of Mining and Chemical Enterprises

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