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, Víctor (2021) Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification. Journal of hydrology, 603 . p. 126873. ISSN 0022-1694, ESSN: 1879-2707

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Official URL: https://doi.org/10.1016/j.jhydrol.2021.126873



Abstract

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.


Item Type:Article
Uncontrolled Keywords:Machine learning, Wetland protection, Groundwater-dependent ecosystems, Wetland management, Big data, Mancha occidental aquifer
Subjects:Sciences > Geology > Hidrology
ID Code:70091
Deposited On:04 Feb 2022 12:14
Last Modified:04 Feb 2022 12:37

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