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http://dx.doi.org/10.7780/kjrs.2020.36.6.1.1

Status of Groundwater Potential Mapping Research Using GIS and Machine Learning  

Lee, Saro (Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM))
Fetemeh, Rezaie (Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM))
Publication Information
Korean Journal of Remote Sensing / v.36, no.6_1, 2020 , pp. 1277-1290 More about this Journal
Abstract
Water resources which is formed of surface and groundwater, are considered as one of the pivotal natural resources worldwide. Since last century, the rapid population growth as well as accelerated industrialization and explosive urbanization lead to boost demand for groundwater for domestic, industrial and agricultural use. In fact, better management of groundwater can play crucial role in sustainable development; therefore, determining accurate location of groundwater based groundwater potential mapping is indispensable. In recent years, integration of machine learning techniques, Geographical Information System (GIS) and Remote Sensing (RS) are popular and effective methods employed for groundwater potential mapping. For determining the status of the integrated approach, a systematic review of 94 directly relevant papers were carried out over the six previous years (2015-2020). According to the literature review, the number of studies published annually increased rapidly over time. The total study area spanned 15 countries, and 85.1% of studies focused on Iran, India, China, South Korea, and Iraq. 20 variables were found to be frequently involved in groundwater potential investigations, of which 9 factors are almost always present namely slope, lithology (geology), land use/land cover (LU/LC), drainage/river density, altitude (elevation), topographic wetness index (TWI), distance from river, rainfall, and aspect. The data integration was carried random forest, support vector machine and boost regression tree among the machine learning techniques. Our study shows that for optimal results, groundwater mapping must be used as a tool to complement field work, rather than a low-cost substitute. Consequently, more study should be conducted to enhance the generalization and precision of groundwater potential map.
Keywords
Groundwater; Machine Learning; GIS; Literature Review;
Citations & Related Records
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  • Reference
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