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Analysis of Forest Carbon Sequestration Management Plan Based on Neural Network Algorithm

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DOI: 10.23977/agrfem.2022.050101 | Downloads: 4 | Views: 175

Author(s)

Minghao Cui 1, Yuntao Fu 1

Affiliation(s)

1 School of Computer Science and Engineering Northeastern University, Shenyang, Liaoning, China

Corresponding Author

Minghao Cui

ABSTRACT

In order to predict the future forest carbon sequestration content, local forest managers can make better transition decisions for appropriate forest management plans, so that the trend of forest carbon sequestration content can be stabilized and ecological balance can be better made. In this paper, a linear regression analysis using matlab software was used to estimate the carbon stocks of the United States, Brazil, Indonesia, and the Democratic Republic of Congo for the past 30 years, and then a RBF neural network model was used to predict the forest carbon stocks of the four countries 100 years in the future. This paper applies the model to these four countries to obtain forest management plans suitable for forest development in these four countries, completing the transition between the old and new forest management plans within ten years, and the future forest carbon stocks will remain balanced and stable after the implementation of the plans.

KEYWORDS

RBF neural network algorithm, Forest Management Plan, Forest carbon stocks

CITE THIS PAPER

Minghao Cui and Yuntao Fu, Analysis of Forest Carbon Sequestration Management Plan Based on Neural Network Algorithm. Agricultural & Forestry Economics and Management (2022) Vol. 5: 1-6. DOI: http://dx.doi.org/10.23977/agrfem.2022.050101.

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