Analysis of influencing factors of carbon sequestration based on BP neural network and clustering decision tree model

Xinlin Yang; Ying Ma

doi:10.54097/hset.v70i.13863

Authors

Xinlin Yang
Ying Ma

DOI:

https://doi.org/10.54097/hset.v70i.13863

Keywords:

BP neural network, sparrow multi-objective optimization algorithm, hierarchical analysis, K-means, meta-cellular automata model.

Abstract

Global warming poses a serious threat to life on Earth, carbon sequestration technologies can mitigate its negative effects. In order to optimize the effect of carbon sequestration, firstly, this paper develops a model using BP neural network to determine what forest management plan is the most effective one at sequestering carbon dioxide. On the basis, the best way of using forest is obtained by using the decision tree model, and then we used K-means cluster analysis algorithm combined with confidence intervals andorigin, determined the transition points of different management areas.

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References

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