Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification



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Martínez Santos, Pedro and Díaz Alcaide, Silvia and Hera Portillo, África de la and Gómez-Escalonilla Canales, Víctor (2021) Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification. Journal of Hydrology, 603 (126873). ISSN 0022-1694, ESSN: 1879-2707

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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 groundwater-dependent ecosystems, thus contributing to integrate wetland protection in land use planning.

Item Type:Article
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CRUE-CSIC (Acuerdos Transformativos 2021)

Uncontrolled Keywords:Machine learning, Wetland protection, Groundwater-dependent ecosystems, Wetland management, Big data, Mancha occidental aquifer
Subjects:Sciences > Geology > Hidrology
ID Code:68970
Deposited On:29 Nov 2021 17:26
Last Modified:18 Feb 2022 08:55

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