Martínez Santos, PedroDíaz Alcaide, SilviaGómez-Escalonilla Canales, VíctorHera Portillo, África de la2023-06-162023-06-162021-120022-169410.1016/j.jhydrol.2021.126873https://hdl.handle.net/20.500.14352/4671Identifying groundwater-dependent ecosystems is the first step towards their protection. This paper presents a machine learning approach that maps groundwater-dependent ecosystems by extrapolating from the characteristics of a small sample of known wetland and non-wetland areas to find other areas with similar geological, hydrological and biotic markers. Explanatory variables for wetland occurrence include topographic elevation, lithology, vegetation vigor, and slope-related variables, among others. Supervised classification algorithms are trained based on the ground truth sample, and their outcomes are checked against an official inventory of groundwater-dependent ecosystems for calibration. This method is illustrated through its application to a UNESCO Biosphere Reserve in central Spain. Support vector machines, tree-based classifiers, logistic regression and k-neighbors classification predicted the presence of groundwater-dependent ecosystems adequately (>96% test and AUC scores). The ensemble mean of the best five classifiers rendered a 90% success rate when computed per surface area. This method can optimize fieldwork during the characterization stage of groundwaterdependent ecosystems, thus contributing to integrate wetland protection in land use planning.engAtribución-NoComercial-SinDerivadas 3.0 Españahttps://creativecommons.org/licenses/by-nc-nd/3.0/es/journal article1879-2707https://doi.org/10.1016/j.jhydrol.2021.126873open access556.3Machine learningWetland protectionGroundwater-dependent ecosystemsWetland managementBig dataMancha occidental aquiferHidrología2508 Hidrología