Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China-Reference-Cited by-同舟云学术

Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China

Published:2023-12-01 Issue:1 Volume:31 Page:1127-1145
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Wang Zitao,Wang Jianping^ORCID,Li Mengling

Funder

the Second Tibetan Plateau Scientific Expedition and Research Program

the National Natural Science Foundation of China

the Qinghai provincial science and technology innovation platform

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-31262-5.pdf

Reference59 articles.

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

2. Ahmad I, Hasan H, Jilani MM, Ahmed SI (2023) Mapping potential groundwater accumulation zones for Karachi City using GIS and AHP techniques. Environ Monit Assess 195:381. https://doi.org/10.1007/s10661-023-10971-x

3. 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. Environ Sci Pollut Res 28:18437–18456. https://doi.org/10.1007/s11356-020-09019-1

4. Arabameri A, Rezaei K, Cerda A et al (2019) GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Sci Total Environ 658:160–177. https://doi.org/10.1016/j.scitotenv.2018.12.115

5. Arabameri A, Pal SC, Rezaie F et al (2021) Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. J Hydrol Reg Stud 36:100848. https://doi.org/10.1016/j.ejrh.2021.100848

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An optimization framework with dimensionality reduction using Markov Chain Monte Carlo and genetic algorithms for groundwater potential assessment;Applied Soft Computing;2024-10