Groundwater potential assessment using GIS-based ensemble learning models in Guanzhong Basin, China-Reference-Cited by-同舟云学术

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Groundwater potential assessment using GIS-based ensemble learning models in Guanzhong Basin, China

Published:2023-05-18 Issue:6 Volume:195 Page:
ISSN:0167-6369
Container-title:Environmental Monitoring and Assessment
language:en
Short-container-title:Environ Monit Assess

Author:

Wang Zitao,Wang Jianping,Yu Dongmei,Chen Kai

Funder

Second Tibetan Plateau Scientific Expedition and Research Program

Key deployment projects of the Chinese academy of sciences

National Natural Science Foundation of China

Innovation Team Foundation of Qinghai Office of Science and Technology

Kunlun Talented People of Qinghai Province, High-end Innovation and Entrepreneurship talents-Leading Talents

Publisher

Springer Science and Business Media LLC

Subject

Management, Monitoring, Policy and Law,Pollution,General Environmental Science,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s10661-023-11388-2.pdf

Reference61 articles.

1. Ahmad, I., Dar, M. A., Fenta, A., et al. (2021). Spatial configuration of groundwater potential zones using OLS regression method. Journal of African Earth Sciences, 177,104147. https://doi.org/10.1016/j.jafrearsci.2021.104147

2. Ahmad, I., Hasan, H., Jilani, M. M., & Ahmed, S. I. (2023). Mapping potential groundwater accumulation zones for Karachi city using GIS and AHP techniques. Environmental Monitoring and Assessment, 195, 381. https://doi.org/10.1007/s10661-023-10971-x

3. Al-Abadi, A. M., Al-Temmeme, A. A., & Al-Ghanimy, M. A. (2016). A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq. Sustain Water Resour Manag, 2, 265–283. https://doi.org/10.1007/s40899-016-0056-5

4. Anand, B., Karunanidhi, D., & Subramani, T. (2021). Promoting artificial recharge to enhance groundwater potential in the lower Bhavani River basin of South India using geospatial techniques. Environmental Science and Pollution Research, 28, 18437–18456. https://doi.org/10.1007/s11356-020-09019-1

5. Arabameri, A., Pal, S. C., Rezaie, F., et al. (2021). Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. Journal of Hydrology: Regional Studies, 36, 100848. https://doi.org/10.1016/j.ejrh.2021.100848

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