A comparison of two artificial neural network methods for predicting the risk of insect pest species establishment in regions where they are not normally found is presented. The ANN methods include a well-known unsupervised learning algorithm and a relatively new supervised constructive method. A New Zealand pest species assemblage as an example was used to compare model predictions. Both methods gave similar results for already established and non-established species.Watts, M.J. and Worner, S.
Neural network models were developed to predict the number of R. padi caught during the autumn fligh...
Abstract The use of multi-layer perceptrons (MLP) to deter-mine the signicance of climatic variable...
Ecological modelling problems have characteristics both featured in other modelling fields and speci...
This paper presents a novel methodology for multi-scale and multi-type spatial data integration in s...
The use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the ...
Predicting future species invasions presents significant challenges to researchers and government ag...
For greater preparedness, pest risk assessors are required to prioritise long lists of pest species ...
Predicting future species invasions presents significant challenges to researchers and government ag...
Predicting future species invasions presents significant challenges to researchers and government ag...
Predicting which species are more likely to invade a region presents significant difficulties to res...
Monitoring networks are improved by additional sensors. Optimal configurations of sensors give bette...
Two methods to predict the abundance of the mayflies Baetis rhodani and Baetis vernus (Insecta, Ephe...
This chapter highlights quantitative methods designed to identify and rank exotic species with poten...
Quantitative methods for pest risk assessment combine sound statistical tools with sound ecological ...
Not AvailableAim : Methodology : Results : Interpretation : Approaches to modelling pest populations...
Neural network models were developed to predict the number of R. padi caught during the autumn fligh...
Abstract The use of multi-layer perceptrons (MLP) to deter-mine the signicance of climatic variable...
Ecological modelling problems have characteristics both featured in other modelling fields and speci...
This paper presents a novel methodology for multi-scale and multi-type spatial data integration in s...
The use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the ...
Predicting future species invasions presents significant challenges to researchers and government ag...
For greater preparedness, pest risk assessors are required to prioritise long lists of pest species ...
Predicting future species invasions presents significant challenges to researchers and government ag...
Predicting future species invasions presents significant challenges to researchers and government ag...
Predicting which species are more likely to invade a region presents significant difficulties to res...
Monitoring networks are improved by additional sensors. Optimal configurations of sensors give bette...
Two methods to predict the abundance of the mayflies Baetis rhodani and Baetis vernus (Insecta, Ephe...
This chapter highlights quantitative methods designed to identify and rank exotic species with poten...
Quantitative methods for pest risk assessment combine sound statistical tools with sound ecological ...
Not AvailableAim : Methodology : Results : Interpretation : Approaches to modelling pest populations...
Neural network models were developed to predict the number of R. padi caught during the autumn fligh...
Abstract The use of multi-layer perceptrons (MLP) to deter-mine the signicance of climatic variable...
Ecological modelling problems have characteristics both featured in other modelling fields and speci...