Applying machine learning techniques to ecological data

This thesis is about modelling carbon flux in forests based on meterological variables using modern machine learning techniques. The motivation is to better understand the carbon uptake process from trees and find the driving factors of it, using totally automated techniques. Data from two British forests were used, (Griffin and Harwood) but finally results were obtained only with Harwood because Griffin had spurious variables in it. Both data sets presented significant challenges: missing values, noise and dimensionality reduction. The missing value problem was addressed with the regularized EM algorithm, whereas for filtering out noise, n-step moving averages was used. A range of different ‘semi-wrapper’ and a filter method have been used for dimensionality reduction: forward selection, backward elimination, best ascent hill climbing, genetic algorithms, evolutionary strategies and correlation-based feature selection. Modelling was done with Multiple Linear Regression, Multilayer Perceptrons and Support Vector Regression. The best model found had at most 83% explained variance. Support Vector Regression and Multilayer Perceptrons had almost the same performance and were better than Multiple Linear Regression, since they managed to capture non-linear details of the process.