Advanced utilization of multi-learning algorithm: ensemble super learner to map groundwater potential for potable mineral water

Abstract

Although mapping the groundwater quality is crucial for people who require groundwater with strict quality standards, the ability to take intensive measurements has been restricted by a lack of groundwater accessibility. Thus, this study aimed to estimate and map the suitability of groundwater quality for use as potable mineral water. We attempted a novel approach by targeting comprehensive qualities for a specific groundwater use and by adopting a super learner that combines multiple different learning algorithms. The super learner generated a groundwater potential map indicating a zone with a high potential for mineral water and it outperformed the base learners by 21%–74%. Estimation results designated appropriate groundwater development locations for mineral water use, and assessment of predictors determined favorable environments. Consequently, the proposed approach presented a possible method for finding groundwater with the required quality for its optimal usage. Furthermore, it provided the possibility of worldwide application.

Keywords:

• GIS
• data-driven modeling
• groundwater quality

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in http://www.gims.go.kr/basicServey.do?tgu=A&sgu=12

Additional information

Funding

This research was supported by the National Research Council of Science and Technology(NST) grant funded by the Korea government(MSIP) (No. CAP-17-05-KIGAM), and partly by Korea Ministry of Environment as ‘The SEM projects; RE2020002470001’.