Parameter inversion estimation in photosynthetic models: impact of different simulation methods

Title:

Parameter inversion estimation in photosynthetic models: impact of different simulation methods

Creator:

Wang, H. B., Ma, M. G., Xie, Y. M., Wang, X. F., and Wang, J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:97d90865-a858-4983-8d4b-9351ba5076c3
uuid:97d90865-a858-4983-8d4b-9351ba5076c3
issn:0300-3604
doi:10.1007/s11099-014-0027-8

Subject:

fotosyntéza, modelování a simulace, photosynthesis, modeling and simulation, optimization algorithms, PN/Ci curve, parameter estimation, photosynthetic models, 2, and 581

Type:

model:article and TEXT

Format:

print, bez média, and svazek

Description:

When we apply ecological models in environmental management, we must assess the accuracy of parameter estimation and its impact on model predictions. Parameters estimated by conventional techniques tend to be nonrobust and require excessive computational resources. However, optimization algorithms are highly robust and generally exhibit convergence of parameter estimation by inversion with nonlinear models. They can simultaneously generate a large number of parameter estimates using an entire data set. In this study, we tested four inversion algorithms (simulated annealing, shuffled complex evolution, particle swarm optimization, and the genetic algorithm) to optimize parameters in photosynthetic models depending on different temperatures. We investigated if parameter boundary values and control variables influenced the accuracy and efficiency of the various algorithms and models. We obtained optimal solutions with all of the inversion algorithms tested if the parameter bounds and control variables were constrained properly. However, the efficiency of processing time use varied with the control variables obtained. In addition, we investigated if temperature dependence formalization impacted optimally the parameter estimation process. We found that the model with a peaked temperature response provided the best fit to the data., H. B. Wang, M. G. Ma, Y. M. Xie, X. F. Wang, J. Wang., and Obsahuje bibliografii

Language:

Multiple languages

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

233-246

Source:

Photosynthetica | 2014 Volume:52 | Number:2

Harvested from:

CDK

Metadata only:

false