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Scientia Silvae Sinicae ›› 2022, Vol. 58 ›› Issue (2): 42-48.doi: 10.11707/j.1001-7488.20220205

• Frontier & Focus: Topic of forest carbon sequestration • Previous Articles     Next Articles

CO2 Concentration in Phyllostachys praecox Stand Inversion Based on GA-BP Neural Network

Zhikang Hou1,Songwei Zeng1,*,Lufeng Mo1,Yufeng Zhou2   

  1. 1. College of Information Engineering, Zhejiang A & F University Hangzhou 311300
    2. College of Environment and Resources Zhejiang A & F University Hangzhou 311300
  • Received:2020-07-02 Online:2022-02-25 Published:2022-04-26
  • Contact: Songwei Zeng

Abstract:

Objective: The purpose of this work is to develop meteorological factor acquisition system of Phyllostachys praecox stand, to obtain the relationship between CO2 concentration and meteorological factors (temperature and humidity, etc.), to discusses the CO2 concentration inversion model based on GA-BP neural network (abbreviated as GA-BP model), and to provide fundamental data for carbon storage, carbon sinks and carbon sequestration capacity of P. praecox stand. Method: According to the relevant principles and methods of micrometeorology and the requirements of dynamic sensing of forest carbon flux, a remote and real-time monitoring system of forest carbon flux data based on embedded system is designed. Taking the mature P. praecox stand, stand as monitored object, this system monitored the environmental data for two months (October ~ November 2019). After analyzing these data, a CO2 concentration inversion model based on genetic classification optimization neural network is proposed. Results: According to the inversion results of GA-BP and BP inversion model, the determinative coefficient R2 of the inversion results of GA-BP inversion model is 0.86, which is 7 percentage points higher than that of BP inversion model. The mean absolute error is 8.12 mg·m-3, which is 2.79 mg·m-3 lower than that of BP inversion model. Compared with the BP inversion model, the GA-BP inversion model has more stable inversion performance and higher inversion accuracy. Conclusion: The P. praecox stand meteorological factor acquisition system can be used to obtain relevant meteorological data. Based on the correlation between CO2 concentration and meteorological factors (temperature and humidity, etc.), the CO2 concentration inversion model based on GA-BP neural network can effectively invert the CO2 concentration data in the survey region.

Key words: ecosystem, carbon flux, GA-BP, carbon storage, Phyllostachys praecox stand

CLC Number: