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• W4302009672 startingPage "128501" @default.
• W4302009672 abstract "This paper aims to review parameters, model techniques, validation methods in groundwater potential field. According to statistics, there are three major model groups used to establish groundwater potential maps. The first model group is a statistic group, including multi-criteria decision making/analytic hierarchy process, frequency ratio, evidence belief function, and weights of evidence. The second model group includes machine learning models, such as random forest, logistic regression, boosted regression tree, and support vector machine. The final group is the hybrid/ensemble models. In groundwater potential mapping studies, 41 thematic layers affect the potential of groundwater. However, hydrological researchers have frequently used eight factors in groundwater potential studies: geology, slope, land use, soil type, drainage density, lineament density, altitude, rainfall. Most previous studies on groundwater potential have used a combination of geographic information system, remote sensing, and machine learning techniques to design the groundwater potential in regions of interest. Data sources are commonly applied to groundwater potential mapping, including satellite, borehole, and geophysical data. The accuracy of groundwater potential maps produced by common machine learning models ranges from 50.0% to 90.1%, while that produced by common statistical models ranges between 59.0% and 90.3%. Interestingly, hybrid/ensemble models’ accuracy interval ranges from 71.0% to 92.0%. Therefore, the review suggests that statistical algorithms and machine learning techniques should be combined, and thematic layers should be increasingly used in mapping groundwater potential maps to achieve high efficiency." @default.
• W4302009672 created "2022-10-06" @default.
• W4302009672 creator A5026898742 @default.
• W4302009672 creator A5034893699 @default.
• W4302009672 creator A5048373136 @default.
• W4302009672 creator A5050265775 @default.
• W4302009672 date "2022-11-01" @default.
• W4302009672 modified "2023-10-01" @default.
• W4302009672 title "Global review of groundwater potential models in the last decade: Parameters, model techniques, and validation" @default.
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