Automatic Generation of Neural Network Topology to Forecast the International Integration Processes

Bilgaeva, Liudmila P.; Sadykova, Erzhena; Филиппов, В. А.

doi:10.1109/fareastcon.2019.8934135

Cited by 2 publications

(6 citation statements)

References 3 publications

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“…We chose artificial neural networks with automatic topology construction, particularly the forecasting method proposed in [31,32] and previously described in [25]. The forecasting process using a neural network consisted of the following steps: preparing the data, training a neural network using a genetic algorithm, and forecasting the composite index and its sub-indices.…”

Section: Neural Network Methodsmentioning

confidence: 99%

“…The environmental-socio-economic system of Buryatia possesses a specific potential and uses the available opportunities for dynamic development. We chose the neural network model as a forecasting method [31,32], as it better solves problems for which statistical methods perform poorly. The results made it possible to generalize the complex processes of the region's environmental-socio-economic development, obtain quantitative assessments of the region's economic state from the green economic development perspective, and determine the further prospects and regional management directions.…”

Section: Introductionmentioning

confidence: 99%

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Green Economy Development Progress in the Republic of Buryatia (Russia)

Bilgaev

Sadykova

Mikheeva

et al. 2022

IJERPH

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Under current conditions, the green economy concept has received a comprehensive response in achieving the sustainable development of regions. However, measuring green economic development progress is dynamic, quantitatively characterized by indicators reflecting various aspects. The difficulty lies in a comprehensive environmental sustainability assessment in a context that includes the territory’s environmental, social, and economic factors. The study aimed to assess the progress of the Republic of Buryatia’s (Russia) “green” economic development. The proposed methodology for constructing a composite index is based on five dimensions’ aggregation—resource efficiency, environmental efficiency, environmental quality of life, natural assets, and institutional factors. The composite index helped generalize the complex processes of the region’s environmental–socio–economic development. Its main feature is the reflection of the environmental specificity of the territory. We built a mid-term forecast of the composite and sub-indices, determined their future trend, and assessed the opportunities and conditions for the fastest transition of the Republic of Buryatia to a green economy. The developed composite index is a key tool for regulating green economic development progress, determining prospects, and region management. This paper attempts to fill the gap in a comprehensive assessment of the Republic of Buryatia’s current situation using a composite index.

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Section: Neural Network Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Green Economy Development Progress in the Republic of Buryatia (Russia)

Bilgaev

Sadykova

Mikheeva

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…Model of an artificial neural network with automatic topology construction; 8. Input of the identified factors using the developed software application [30]; 9. Result: forecast of eco-socio-economic development of the Baikal region for the future to determine the future state.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, using the Baikal region example, this method was tested and showed good results. Therefore, to forecast the eco-socio-economic development of the Baikal region, we chose artificial neural networks with automatic topology construction, particularly the forecasting method proposed in [27,28], and the software developed on its basis [30]. This forecasting technique is based on the use of a multilayer perceptron (MLP) [39] in combination with evolutionary calculations [40,41].…”

Section: Neural Network Methodsmentioning

confidence: 99%

“…The region's eco-socio-economic system characterizes by the fact that it has a certain potential and uses the available opportunities for dynamic development. We choose neural network models as a forecasting method [27,28] because it is good at solving problems where statistical methods work poorly. It predicted future trends in the development of the region's system and gave recommendations for policy changes and proposed regulatory mechanisms to promote the introduction and use of the green economy principles.…”

Section: Introductionmentioning

confidence: 99%

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Baikal Region (Russia) Development Prospects Based on the Green Economy Principles

Bilgaev

Dong

et al. 2020

Sustainability

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The problems of preserving the natural environment are relevant for unique natural complexes of territories, such as the Baikal region (Russia). There are few studies devoted to forecasting the green economic development of the eco-socio-economic system of the Baikal region. The previous studies are mainly dedicated to the theory of forming the green economy in the Baikal region. This study aimed to forecast and determine future trends in the Baikal region’s eco-socio-economic development based on the green economy. The methodological approach to assess the Baikal region development prospects is based on determining the factors affecting the relationship between various eco-socio-economic components using multiple regression models. We constructed the forecast using the artificial neural networks method, and we assessed the possible impact of anthropogenic factors on the Baikal region’s eco-socio-economic system in the future. Based on the obtained forecast, we proposed a mechanism for regulating the green economy formation in the region. The regional governments of the Baikal region should perform the management function. This requires establishing a special interregional Green Development Fund to restructure incoming public funds by redistributing financial flows from contaminating brown to green industries and develop a state program for the Green Development of the Baikal region. The constructed forecast is informational support for decision making for the long-term development of the economy during the transition to the green economy, the development and justification of program activities, and the future’s main strategic priorities.

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Automatic Generation of Neural Network Topology to Forecast the International Integration Processes

Cited by 2 publications

References 3 publications

Green Economy Development Progress in the Republic of Buryatia (Russia)

Green Economy Development Progress in the Republic of Buryatia (Russia)

Baikal Region (Russia) Development Prospects Based on the Green Economy Principles

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