Martínez Santos, PedroAristizábal, H. F.Gómez Escalonilla, Víctor2023-06-172023-06-1720210022-169410.1016/j.jhydrol.2021.126026https://hdl.handle.net/20.500.14352/7846In a context of rapid change, mapping aquatic ecosystems is crucial for their protection, particularly in regions of the world where a comprehensive inventory of water bodies is yet to be developed. This paper presents a machine learning methodology for predictive aquatic ecosystem mapping. Two state-of-the-art classifiers were used, namely support vector machines (SVM) and random forests (RFC). These were implemented in a GIS environment and trained on a sample of 350 points of a western Colombian catchment, where the presence or absence or water was known beforehand. Sixty percent of the dataset was used to train the algorithms and the remainder was used to calibrate predictive accuracy. Both classifiers rendered a realistic picture of the catchment’s water bodies. RFC obtained a mean test score in excess of 0.93 and an area under the receiver operating characteristic curve of 0.95. SVM scored 0.89 and 0.89, respectively, after optimization. Performance was better overall in flat regions. SVM identified correctly over 87% of the surface area of lentic and 82% of lotic water bodies, while RFC scored over 80% in both cases. In mountain regions precision dropped to 69% and 62% for SVM and 65% and 76% for RFC. This leads to three conclusions: (a) the method provides satisfactory results at the regional scale and is versatile enough to export to other settings; (b) local-scale detail cannot be captured in the absence of an accurate high-resolution digital elevation model, particularly in mountain areas; (c) complementing standard machine learning metrics with ad hoc indicators is likely needed in cases where the input dataset consists solely of unambiguous examples.engjournal articlehttps://doi.org/10.1016/j.jhydrol.2021.126026restricted access556.5Machine learningFreshwaterEcosystem servicesBig dataSupervised classificationHidrología2508 Hidrología