RT Journal Article
T1 Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
A1 Gómez-Escalonilla Canales, Víctor
A1 Vogt, Marie-Louise
A1 Destro, Elisa
A1 Isseini, Moussa
A1 Origgi, Giaime
A1 Djoret, Daira
A1 Martínez Santos, Pedro
A1 Holecz, Francesco
AB This paper presents a machine learning method to map groundwater potential in crystalline domains. First, a spatially-distributed set of explanatory variables for groundwater occurrence is compiled into a geographic information system. Twenty machine learning classifiers are subsequently trained on a sample of 488 boreholes and excavated wells for a region of eastern Chad. This process includes collinearity, cross-validation, feature elimination and parameter fitting routines. Random forest and extra trees classifiers outperformed other algorithms (test score > 0.80, balanced score > 0.80, AUC > 0.87). Fracture density, slope, SAR coherence (interferometric correlation), topographic wetness index, basement depth, distance to channels and slope aspect proved the most relevant explanatory variables. Three major conclusions stem from this work: (1) using a large number of supervised classification algorithms is advisable in groundwater potential studies; (2) the choice of performance metrics constrains the relevance of explanatory variables; and (3) seasonal variations from satellite images contribute to successful groundwater potential mapping.
PB Taylor and Francis
SN 1010-6049
YR 2021
FD 2021
LK https://hdl.handle.net/20.500.14352/6765
UL https://hdl.handle.net/20.500.14352/6765
LA eng
NO Ministerio de Ciencia e Innovación (MICINN)
DS Docta Complutense
RD 26 dic 2025