Artificial neural networks and emergent pests

Project status: 
In Progress
Project Leader(s): 
Dr Sue Worner, Lincoln University
Team Member(s): 
Professor Nik Kasabov, AUT
Team Member(s): 
Dr Mike Watts, Sydney University
Team Member(s): 
Dr Dean Paini, CSIRO, Australia
We're using predictive models to fight against invasive species.

We are using Artificial Neural Network (ANN) analytical methods to identify emergent pest problems and create risk indices for specific species.

ANNs are computational tools that have novel application to problems in invasion biology.

Self organising maps have been used to analyse a database comprising the world distribution of 844 insect pest species to rank their establishment potential in New Zealand based on the strength of their association with geographic pest assemblages. Back-propagation neural networks have been used to further inform the risk analysis process by identifying variables that best explain current species distribution and use that information to predict presence in areas where the species is not normally found.

To date, two predictive models have been developed. We are combining them with statistical and computational methods to model potential invasive species establishment and spread throughout New Zealand. 

Back to Intelligent Systems for Biosecurity homepage 

AttachmentSize
Gevrey et al. (2006) Journ. Ec. Mod. Vol.197 361-372.pdf1.05 MB
Watts et al. (2006)Int.Journ_.Inf_.Tech_. Vol. 12(6) 35-42.pdf182.71 KB
Watts et al. (2007) Eco. Informatics, 364-74.pdf640.37 KB
Worner et al. (2006) JAE. Vol. 43. 858-867.pdf305.54 KB