Predicting artificially drained areas by means of a selective model ensemble

A stepwise algorithm then selects models from the library, based on their predictions on a hillclimb dataset. The results show that models trained using principal components generally yielded a better performance than the models trained with the raw covariates. Furthermore, the best results were obtained when only a random fraction of the models was available for selection at each step. The covariates that were most important for the prediction of artificially drained areas mostly related to soil properties and topography. Overall, the ensemble predicted artificially drained areas with an accuracy of 76.5%. The study demonstrates machine learning as an accurate method for mapping artificially drained areas, which is likely to benefit both farmers and decision makers.