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

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  • Zhang, WenJun
  • Zhang, XiYan

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.

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  • Zhang, WenJun & Zhang, XiYan, 2008. "Neural network modeling of survival dynamics of holometabolous insects: A case study," Ecological Modelling, Elsevier, vol. 211(3), pages 433-443.
    • Handle: RePEc:eee:ecomod:v:211:y:2008:i:3:p:433-443
    DOI: 10.1016/j.ecolmodel.2007.09.026
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    4. Zhang, WenJun & Bai, ChangJun & Liu, GuoDao, 2007. "Neural network modeling of ecosystems: A case study on cabbage growth system," Ecological Modelling, Elsevier, vol. 201(3), pages 317-325.
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    1. Varga, M. & Csukas, B., 2017. "Generation of extensible ecosystem models from a network structure and from locally executable programs," Ecological Modelling, Elsevier, vol. 364(C), pages 25-41.

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