Neural network modeling of survival dynamics of holometabolous insects: A case study

WenJun Zhang and XiYan Zhang

Ecological Modelling, 2008, vol. 211, issue 3, 433-443

Abstract: Survival process and mortality distribution of holometabolous insects were hard to be described by mechanistic models due to their distinctive development stages in the life cycle. Neural networks are flexible approximators for linear or nonlinear ecological systems. This study aimed to evaluate the effectiveness and performance of BP ANN (feed-forward backpropagation artificial neural network) and conventional models in modeling the survival process and mortality distribution of a holometabolous insect, Spodoptera litura F. (Lepidoptera: Noctuidae). Training data on survival process and mortality distribution of S. litura were recorded under different temperatures. BP ANN, three empirical models, five probabilistic density functions, a multi-stage based dynamic model, and a trend surface model were used to modeling the time changing and temperature dependent relationships of the insect. Overall performances were compared among these models.

Keywords: Artificial neural network; Modeling; Empirical models; Probabilistic density functions; Holometabolous insects; Survival dynamics (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S030438000700498X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:211:y:2008:i:3:p:433-443

DOI: 10.1016/j.ecolmodel.2007.09.026

Access Statistics for this article

Ecological Modelling is currently edited by Brian D. Fath

More articles in Ecological Modelling from Elsevier
Bibliographic data for series maintained by Catherine Liu ().