2026-08-20T17:00:13Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/46712024-06-12T15:15:07Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Martínez Santos, Pedro
author
Díaz Alcaide, Silvia
author
Gómez-Escalonilla Canales, Víctor
author
Hera Portillo, África de la
2023-06-16T14:19:17Z
2023-06-16T14:19:17Z
2021-12
0022-1694
10.1016/j.jhydrol.2021.126873
https://hdl.handle.net/20.500.14352/4671
1879-2707
https://doi.org/10.1016/j.jhydrol.2021.126873
Identifying 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.
eng
Atribución-NoComercial-SinDerivadas 3.0 España
Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification
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