Modeling of stomatal conductance for Populus euphratica using Back Propagation and Ball-Berry model
LI Peidu;SI Jianhua;FENG Qi;YU Tengfei;ZHAO Chunyan;Key Laboratory of Ecohydrology of Inland River Basin ,Alxa Desert Ecohydrological Experimental Research Station ,Cold and Arid Regions Environmental and Engineering Research Institute,Chinese Academy of Sciences;University of Chinese Academy of Sciences;
Based on measured data of Populus euphratica stomatal conductivity and the environmental factors during period of May to September,2014,the seasonal variation characteristics of P. euphratica stomatal conductance were analyzed and the simulation research was carried out based on Bp neural network. The results show that P. euphratica stomatal conductance changed with seasons and peak slightly lagged. Correlation coefficients between P. euphratica stomatal conductance and PAR in different seasons were higher,and correlation with other environmental factors in different season changed. Bp neural network model simulation of P. euphratica stomatal conduction was good,coefficients of determination( R2) between the model simulation value and the actual observed value of P. euphratica stomatal conductance in spring,summer and autumn based on 1: 1 line with were0. 3818,0. 5392 and 0. 9078,respectively,Root Mean Square Error( RMSE) respectively were 0. 1265,0.0541 and 0. 0755mol·m-2·s~(-1). The predicted value R2 of P. euphratica stomatal conductance was 0. 2578,0. 3558 and 0. 5807 in spring,summer and autumn by the model of Ball-berry,RMSE respectively was 0.0517,0. 1665 and 0. 1192mol·m~(-2)·s~(-1). The accuracy of P. euphratica stomatal conductance simulation was poor in Ball-berry model. Bp neural network model for different seasons of P. euphratica stomatal conductance was simulated under PAR. It was found that the Bp neural network model simulation of stomatal conductance under different levels of PAR had high precision; stomatal conductance simulation value and observation value in spring,summer and autumn based on the 1: 1 line R2 respectively was 0. 5129,0. 5465 0. 6739,RMSE respectively were 0. 0407,0. 0752 and 0. 1406mol·m-2·s~(-1). While the Ball-berry model of the three seasons of R2 were 0. 1796,0. 2474 and 0. 5477,RMSE was 0. 1656,0. 1409 and 0. 0624mol·m-2·s~(-1). It showed that the Bp neural network model for P. euphratica stomatal conductance simulation is better.
【Fund】： 国家自然科学基金(91025024);; 中国科学院西部之光项目;中国科学院重点部署项目(KZZD-EW-04-05)资助
【CateGory Index】： S792.11
【CateGory Index】： S792.11