Mapping Groundwater Prospective Areas Using Remote Sensing and GIS-Based Data Driven Frequency Ratio Techniques and Detecting Land Cover Changes in the Yellow River Basin, China

Shuhang Li, Mohamed Abdelkareem () and Nassir Al-Arifi
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Shuhang Li: Ural Institution, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Mohamed Abdelkareem: Geology Department, South Valley University, Qena 83523, Egypt
Nassir Al-Arifi: Chair of Natural Hazards and Mineral Resources, Geology and Geophysics Department, King Saud University, Riyadh 68953, Saudi Arabia

Land, 2023, vol. 12, issue 4, 1-20

Abstract: Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. Remote sensing data can predict the location of potential water resources. The current study was conducted in China’s Yellow River region, Ningxia Hui Autonomous Region (NHAR). Through the use of a GIS-based frequency ratio machine learning technique, nine layers of evidence influenced by remote sensing data were generated and integrated. The layers used are soil characteristics, aspect, and roughness index of the terrain, drainage density, elevation, lineament density, depressions, rainfall, and distance to the river from the location. Six groundwater prospective zones (GWPZs) were found to have very low (13%), low (30%), moderate (25%), high (16%), very high (11%), and extreme potentiality (5.26%) values. According to well data used to validate the GWPZs map, approximately 40% of the wells are consistent to very high to excellent zones. Information about groundwater productivity was gathered from 150 well locations. Using well data that had not been used for model training, the resulting GWPZs maps were validated using area-under-the-curve (AUC) analysis. FR models have an accuracy rating of 0.759. Landsat data were used to characterize the study area’s changes in land cover. The spatiotemporal differences in land cover are detected and quantified using multi-temporal images which revealed changes in water, agricultural, and anthropogenic activities. Overall, combining different data sets through a GIS can reveal the promising areas of water resources that aid planners and managers.

Keywords: groundwater; remote sensing; GIS; data-driven modeling (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:4:p:771-:d:1110162

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