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《山东大学学报(理学版)》 ›› 2021, Vol. 56 ›› Issue (5): 33-41.doi: 10.6040/j.issn.1671-9352.0.2021.141

• • 上一篇    

基于遗传算法优化BP神经网络的青藏高原海北高寒湿地CO2通量模拟及其影响因子

曹镓玺1,王鑫2,雷光春1*   

  1. 1.北京林业大学生态与自然保护学院, 北京 100083;2.北京师范大学地理科学学部遥感科学与工程研究院, 北京 100875
  • 发布日期:2021-05-13
  • 作者简介:曹镓玺(1997— ),男,硕士研究生,研究方向为湿地碳过程. E-mail:aircraft@bjfu.edu.cn*通信作者简介:雷光春(1960— ),男,教授,博士生导师,研究方向为湿地保护与管理. E-mail:guangchun.Lei@foxmail.com

Simulation of alpine wetlands CO2 flux and its influencing factors based on BP neural network optimized by genetic algorithm in Qinghai-Tibet Plateau

CAO Jia-xi1, WANG Xin2, LEI Guang-chun1*   

  1. 1. School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China;
    2. Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
  • Published:2021-05-13

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摘要： 构建了使用潜热通量、显热通量、空气温度、总辐射、有效辐射、土壤温度、土壤体积含水量来模拟湿地生态系统CO2排放通量的3层BP神经网络。在确定BP神经网络拓扑结构之后,使用遗传算法取得了BP神经网络训练的最优初始阈值和权值。采用2 190组青藏高原海北高寒湿地生态系统中实测CO2通量及相应的训练数据归一化处理后,对所建立的BP神经网络进行训练和验证。验证结果显示,训练组、验证组和测试组的回归系数分别为0.942、0.935和0.938,总体回归系数为0.941,模拟值与实测值之间的均方根误差为0.92%,高寒湿地生态系统中CO2模拟通量为1.207~13.767 g/(m2·d),均值为6.008 g/(m2·〓d)。相关性分析结果显示,青藏高原海北高寒湿地生态系统中CO2通量与输入神经元的相关系数r表现为:土壤温度(0.77)>空气温度(0.72)>土壤体积含水量(0.39)>潜热通量(0.30)>显热通量(0.29)>总辐射(0.09)>有效辐射(-0.02)(P<0.01)。青藏高原海北高寒湿地生态系统中CO2通量主导因子为土壤温度和空气温度,后续建模工作中应提高这二者的权重。

关键词: CO2排放通量, Matlab, 遗传算法, BP神经网络, 湿地生态系统

Abstract: A three-layer BP neural network was constructed to simulate CO2 emission flux of wetlands ecosystem by using latent heat flux, sensible heat flux, air temperature, total radiation, effective radiation, soil temperature and soil volume water content. After determining the topological structure of BP neural network, the optimal initial threshold and weight of BP neural network training are obtained by genetic algorithm. The established BP neural network is trained and verified by normalizing 2 190 sets of measured CO2 fluxes and corresponding training data in wetlands ecosystem in Haibei alpine wetlands of Qinghai-Tibet Plateau. The validation results show that the regression coefficients of training group, validation group and test group are 0.942, 0.935 and 0.938, respectively. The overall regression coefficient is 0.941, the root mean square error between simulated and measured values is 0.92%, and the simulated CO2 flux in wetlands ecosystem is 1.207-13.767 g/(m2·d), with an average value of 6.008 g/(m2·d). Correlation analysis shows that the correlation coefficient r between CO2 flux and input neurons in alpine wetlands ecosystem is: soil temperature(0.77)> air temperature(0.72)> soil volume water content(0.39)> latent heat flux(0.30)> sensible heat flux(0.29)> total radiation(0.09)> effective radiation(0.02)(P<0.01). The dominant factors of CO2 flux in alpine wetlands ecosystem are soil temperature and air temperature, and the weights of these two factors should be increased in the subsequent modeling work.

Key words: CO2 fluxe, Matlab, genetic algorithm, BP neural network, wetlands ecosystem

中图分类号: 

  • X171
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