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Chinese Journal of Applied Ecology ›› 2023, Vol. 34 ›› Issue (8): 2123-2132.doi: 10.13287/j.1001-9332.202308.019

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Instantaneous sap flow velocity simulation of Euonymus bungeanus based on neural network optimization model

ZHOU Peng1, HAN Lei2,3,4*, PENG Ling1, LIU Lili2,3,4, WANG Nana2,3,4, MA Jun1, MA Yunlei2,3,4   

  1. 1College of Agriculture, Ningxia University, Yinchuan 750021, China;
    2School of Geography and Planning, Ningxia University, Yinchuan 750021, China;
    3China-Arab Joint International Research Laboratory for Featured Resources and Environmental Governance in Arid Regions, Yinchuan 750021, China;
    4Ningxia Key Laboratory of Resource Environmental Regulation in Arid Region, Yinchuan 750021, China
  • Received:2023-02-21 Accepted:2023-05-25 Online:2023-08-15 Published:2024-02-15

Abstract: The sap flow of trees is complex and difficult to express with multivariate linear or empirical models. A simple and feasible method on the basis of understanding sap flow variation to simulate its variation with environmental factors is of special importance for quantitatively analyzing forest ecohydrological processes and regional water demand. In this study, with one of the shelter forest species Euonymus bungeanus in the east sandy land of Yellow River in Ningxia as the research object, we continuously measured the trunk sap flow velocity by thermal diffusion sap flow meter, and analyzed the effects of environmental factors on stem sap flow. We used the particle swarm optimization (PSO) and sparrow search algorithm (SSA) optimized neural network model to predict sap flow velocity of E. bungeanus. Results showed that the main environmental factors influencing sap flow were solar radiation, vapor pressure deficit, air temperature, and relative humidity, with the influencing importance of 32.5%, 25.3%, 22.0% and 16.1%, respectively. The response process between sap flow and environmental factors presented a hysteresis loop relationship. The optimized BP, Elman and ELM neural network models improved the comprehensive evaluation index (GPI) by 1.5%, 30.0% and 5.3%, respectively. Compared with the PSO-Elman and SSA-ELM optimization models, the SSA-BP optimization model had the best prediction results with an improvement of 1.0% and 23.2% in GPI, respectively. Therefore, the prediction results of the BP neural network model based on the sparrow search algorithm could be used as an optimal model for predicting instantaneous sap flow velocity of E. bungeanus.

Key words: neural network, optimization algorithm, instantaneous sap flow, simulation, prediction, Euonymus bungeanus.