期刊
GEOCARTO INTERNATIONAL
卷 37, 期 25, 页码 8924-8951出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2021.2007298
关键词
Remote sensing; groundwater exploration; machine learning; Lake Chad basin
类别
资金
- Government of Chad
- Swiss Agency for Development and Cooperation (SDC)
- Spain's Ministerio de Ciencia, Innovacion y Universidades [RTI2018-099394-B-I00]
- FPI grant from the Ministerio de Ciencia e Innovacion [PRE2019-090026]
This study uses machine learning method for mapping groundwater potential in crystalline domains, finding that random forest and extra trees classifiers perform the best among twenty classifiers tested, and the choice of performance metrics influences the relevance of explanatory variables, while seasonal variations from satellite images contribute to successful groundwater potential mapping.
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.
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