Predicting artificially drained areas by means of a selective model ensemble

Farmers often install subsurface drainage systems to improve yields on wet soils, which has large impacts on the hydrological system. The present study uses an ensemble of machine learning models to map the extent of artificially drained areas in Denmark. The prediction is based on 745 field observations, of which one third is held out for evaluation, and 46 covariate layers. A library of 308 models is trained using 77 machine learning methods and four datasets containing either a combination of topographic variables, satellite imagery, soil properties and land use information or principal components based on these variables. 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.