Neural network modeling of ecosystems: A case study on cabbage growth system

• Published in: Ecological modelling Vol. 201; no. 3; pp. 317 - 325
• Main Authors: Zhang, WenJun, Bai, ChangJun, Liu, GuoDao
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 10-03-2007; Elsevier
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; Brassica; Cabbage; Differential equation; Ecosystem; Elman network; Fundamental and applied biological sciences. Psychology; General aspects; General aspects. Techniques; Linear network; Methods and techniques (sampling, tagging, trapping, modelling...); Modeling; Neural networks; Synecology; Elman network; Neural networks; Differential equation; Ecosystem; Linear network; Cabbage; Modeling; Growth; Neural network; Brassica oleracea var. capitata; Case study; Neural networks Elman network Linear network Differential equation Modeling Ecosystem Cabbage; Cruciferae; Dicotyledones; Angiospermae; Spermatophyta; Models
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2006.09.022

A deep understanding on the intrinsic mechanism is required to develop a highly specialized mechanistic model for ecosystem dynamics. However, it is usually hard to do for most of the ecological and environmental problems, because of the lack of a consistent theoretical background. Neural networks are universal and flexible models for linear and non-linear systems. This paper aimed to modeling an ecosystem using neural network models and the conventional model, and assessing their effectiveness in the dynamic simulation of ecosystem. Elman neural network model, linear neural network model, and linear ordinary differential equation were developed to simulate the dynamics of Chinese cabbage growth system recorded in the field. Matlab codes for these neural network models were given. Sensitivity analysis was conducted to detect the robustness of these models.
The results showed that Elman neural network model could simulate the multivariate non-linear system at the desired accuracy. Linear neural network model may simulate such a non-linear system only in certain conditions. Conventional linear ordinary differential equation yielded divergent outputs in the dynamic simulation of the multivariate non-linear system.
Sensitivity analysis indicated that the learning rate influenced the simulation performance of linear neural network model. Transfer function of the second layer in Elman neural network model would influence the simulation performance of this model, but little influence was produced when other functions were changed. Elman neural network was proven to be a robust model. Sensitivity analysis showed that the different choices of functions and parameters in neural network model would influence the performance of simulation. Sensitivity analysis is therefore powerful to detect the robustness and stability of neural network models.