Genetic algorithms for optimisation of predictive ecosystems models based on decision trees and neural networks

The selection of appropriate input variables in predictive ecological modelling is an important issue since numerous variables can be involved. Most of the input variables cannot be omitted because it results in a significant loss of information. The collection of field data on the other hand is both time-consuming and expensive. Rigorous methods are therefore needed to decide which explanatory variables or combinations of variables should enter the model. Appropriate selection of input variables is not only important for modelling objectives as such, but also to ensure reliable decision-support in river management and policy-making. In this paper, the use of genetic algorithms is explored to automatically select the relevant input variables for classification trees and artificial neural networks (ANNs), predicting the presence or absence of benthic macroinvertebrate taxa. The applied database consisted of measurements from 360 sites in unnavigable watercourses in Flanders, Belgium. The measured variables are a combination of physical-chemical, eco-toxicological and structural ones. The predictive power of the models was assessed on the basis of three performance measures: root mean squared error (RMSE), correctly classified instances (CCI) and Cohen's kappa. The selected genetic algorithm introduced different sets of input variables to the models and compared their predictive power to select the optimal combination of input variables. With this technique, the number of input variables could be reduced from 17 to three to six for classification trees and to five to eleven for ANNs. The prediction success increased significantly based on a statistical test. Overall, a better performance was detected for decision trees. The most appropriate way to assess the performance of the models was a combination of the CCI and Cohen's kappa. By means of this variable selection stage, the key variables that determine the presence or absence of benthic macroinvertebrate taxa in Flanders could also be identified. (c) 2005 Elsevier B.V. All rights reserved.