Predicting grassland community changes with an artificial neural network model Academic Article

abstract

• Artificial neural networks are parallel processing systems with the ability to learn by example and generalize from inferred patterns. In this application, a neural network model of the feedforward, backpropagation type is designed to predict future community composition from knowledge of present climatic factors and species cover. Training and testing data are drawn from a 30-year record of the environmental and vegetative variables of a grassland community. The resulting trained network is capable of forecasting accurately up to 4 years into the future. The results indicate a potential usefulness of neural network technology for non-mechanistic modeling in ecological research and management. 1996 Elsevier Science B.V. All rights reserved.

authors

• Smeins, Fred

published proceedings

• ECOLOGICAL MODELLING

author list (cited authors)

• Tan, S. S., & Smeins, F. E.

citation count

• 65

complete list of authors

• Tan, SS||Smeins, FE

publication date

• January 1996

publisher

• Elsevier  Publisher

published in

• ECOLOGICAL MODELLING  Journal

keywords

• Artificial Intelligence
• Grassland Ecosystems
• Vegetation Dynamics

Digital Object Identifier (DOI)

• 10.1016/0304-3800(94)00131-6

start page

• 91

end page

• 97

volume

• 84

issue

• 1-3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2F0304-3800%2894%2900131-6