Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

Wan, Jie; Yu, Baochun

doi:10.3389/fenrg.2022.1087526

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…Neural network models are widely used in various fields such as prediction, pattern recognition, and risk assessment [16][17][18]. The BP neural network model is a multi-layer feedforward network trained according to error backpropagation [19].…”

Section: Construction Of a Carbon Dioxide Prediction Modelmentioning

confidence: 99%

Influencing the Variable Selection and Prediction of Carbon Emissions in China

Chang,

Jiao,

Wang

2023

Sustainability

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In order to study the changing rule of carbon dioxide emissions in China, this paper systematically focused on their current situation, influencing factors, and future trends. Firstly, the current situations of global carbon dioxide emissions and China’s carbon dioxide emissions were presented via a visualization method and their characteristics were analyzed; secondly, the random forest regression model was used to screen the main factors affecting China’s carbon emissions. Considering the different aspects of carbon emissions, 29 influencing factors were determined and 6 main influencing factors were determined according to the results of the random forest regression model. Then, a prediction model for carbon emissions in China was established. The BP neural network model, multi-factor LSTM time series model, and CNN-LSTM model were compared on the test set and all of them passed the test. However, the goodness of fit of the CNN-LSTM model was about 0.01~0.02 higher than the other two models and the MAE and RMSE of the CNN-LSTM model were about 0.01~0.03 lower than those of the other two models. Thus, it was selected to predict China’s carbon dioxide emissions. The predicted results showed that the peak of China’s carbon emissions will be around 2027 and the peak of these emissions will be between 12.9 billion tons and 13.2 billion tons. Overall, the paper puts forward reasonable suggestions for China’s low-carbon development and provides a reference for an adjustment plan of energy structure.

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Section: Construction Of a Carbon Dioxide Prediction Modelmentioning

confidence: 99%

Influencing the Variable Selection and Prediction of Carbon Emissions in China

Chang,

Jiao,

Wang

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In the process of learning, information can be transmitted in the forward direction while error can be transmitted in the reverse direction, and each layer can also be interconnected with each other. The acceptance of information can be determined by the connection weight of the network (Wan and Yu, 2023). The specific workflow is: the neurons in the input layer acquire the signal from the outside and receive it, and then transmit it to the hidden layer.…”

Section: Bp Neural Network Modelmentioning

confidence: 99%

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Huang

2023

Front. Ecol. Evol.

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Previous machine learning models usually faced the problem of poor performance, especially for aquatic product supply chains. In this study, we proposed a coupling machine learning model Shapely value-based to predict the CCL demand of aquatic products (CCLD-AP). We first select the key impact indicators through the gray correlation degree and finally determine the indicator system. Secondly, gray prediction, principal component regression analysis prediction, and BP neural network models are constructed from the perspective of time series, linear regression and nonlinear, combined with three single forecasts, a combined forecasting model is constructed, the error analysis of all prediction model results shows that the combined prediction results are more accurate. Finally, the trend extrapolation method and time series are combined to predict the independent variable influencing factor value and the CCLD-AP from 2023 to 2027. Our study can provide a reference for the progress of CCLD-AP in ports and their hinterland cities.

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“…This paradigm shift finds validation across multiple research studies. For instance, studies have showcased the effectiveness of deep learning models such as BP neural networks (Cai et al, 2020;Wu et al, 2021;Liu, 2022;Wan and Yu, 2023) and Multi layer perception (MLP) (Lian et al, 2021;Li et al, 2022;Wang and Zhang, 2023). The appeal of these deep learning models lies in their exceptional performance and versatility.…”

Section: Introductionmentioning

confidence: 99%

Early warning research on enterprise carbon emission reduction credit risk based on deep learning model under unbalanced data

Long,

Chen

2023

Front. Energy Res.

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To enhance the precision of predicting enterprise credit risk related to carbon emission reduction, this study focuses on publicly traded companies. It introduces a risk early warning model grounded in MLP deep learning. Primarily, this research employs the FA-TOPSIS fusion model to comprehensively assess the credit risk associated with carbon emission reduction in enterprises. Subsequently, it employs K-means clustering to compute enterprise similarities, which forms the basis for supervised learning in the MLP model to assign credit risk grade labels. Furthermore, the study tackles the challenge of imbalanced enterprise grade distribution using the ADASYN over-sampling algorithm. Ultimately, the effectiveness of the model proposed herein is confirmed through a series of multi-model comparison experiments. The results show that: First, carbon emission reduction indicators exhibit differing degrees of influence on enterprises at various credit risk levels. Notably, the most influential indicator is carbon emission intensity, while the development capacity indicator exerts the least influence. Second, the adoption of the XGBoost algorithm for screening carbon emission reduction indicators significantly enhances the prediction accuracy of the early warning model by 4.27%. Third, compared to other models, the MLP model achieves an impressive prediction accuracy of 99.48%, representing an average improvement of 15.24%. These results underscore the model’s feasibility and its potential to provide technical support for financial institutions and government entities in conducting credit ratings for enterprise carbon emission reduction.

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Early warning of enterprise financial risk based on improved BP neural network model in low-carbon economy

Cited by 5 publications

References 17 publications

Influencing the Variable Selection and Prediction of Carbon Emissions in China

Influencing the Variable Selection and Prediction of Carbon Emissions in China

An improved machine learning model Shapley value-based to forecast demand for aquatic product supply chain

Early warning research on enterprise carbon emission reduction credit risk based on deep learning model under unbalanced data

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