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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

Author

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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)

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.

Suggested Citation

  • Shuhang Li & Mohamed Abdelkareem & Nassir Al-Arifi, 2023. "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," Land, MDPI, vol. 12(4), pages 1-20, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:771-:d:1110162
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    References listed on IDEAS

    as
    1. Mohamed Abdekareem & Nasir Al-Arifi & Fathy Abdalla & Abbas Mansour & Farouk El-Baz, 2022. "Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions," Sustainability, MDPI, vol. 14(13), pages 1-26, June.
    2. Lina Mi & Juncang Tian & Jianning Si & Yuchun Chen & Yinghai Li & Xinhe Wang, 2020. "Evolution of Groundwater in Yinchuan Oasis at the Upper Reaches of the Yellow River after Water-Saving Transformation and Its Driving Factors," IJERPH, MDPI, vol. 17(4), pages 1-17, February.
    3. Mohamed Abdelkareem, 2017. "Targeting flash flood potential areas using remotely sensed data and GIS techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(1), pages 19-37, January.
    4. Aminreza Neshat & Biswajeet Pradhan, 2015. "An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(1), pages 543-563, March.
    5. Huadan Fan & Yuefeng Lu & Yulong Hu & Jun Fang & Chengzhe Lv & Changqing Xu & Xinyi Feng & Yanru Liu, 2022. "A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model," Sustainability, MDPI, vol. 14(13), pages 1-17, June.
    6. Saeid Janizadeh & Mohammadtaghi Avand & Abolfazl Jaafari & Tran Van Phong & Mahmoud Bayat & Ebrahim Ahmadisharaf & Indra Prakash & Binh Thai Pham & Saro Lee, 2019. "Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
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    1. Mohamed Abdelkareem & Fathy Abdalla & Fahad Alshehri & Chaitanya B. Pande, 2023. "Mapping Groundwater Prospective Zones Using Remote Sensing and Geographical Information System Techniques in Wadi Fatima, Western Saudi Arabia," Sustainability, MDPI, vol. 15(21), pages 1-21, November.

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