Relationship between groundwater quality and distance to fault using adaptive neuro fuzzy inference system (ANFIS) and geostatistical methods (case study: North of Fars Province)-Reference-Cited by-同舟云学术

Relationship between groundwater quality and distance to fault using adaptive neuro fuzzy inference system (ANFIS) and geostatistical methods (case study: North of Fars Province)

Published:2019-02-26 Issue:5 Volume:27 Page:529-538
ISSN:2366-3286
Container-title:Spatial Information Research
language:en
Short-container-title:Spat. Inf. Res.

Author:

Aghajari Maryam,Mozayyan Maleeha,Mokarram Marzieh^ORCID,Chekan Alireza Amirian

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computers in Earth Sciences,Computer Science Applications,Geography, Planning and Development

Link

http://link.springer.com/article/10.1007/s41324-019-00253-5/fulltext.html

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3. Zheng, Z., Zhang, F., Chai, X., Zhu, Z., & Ma, F. (2009). Spatial estimation of soil moisture and salinity with neural Kriging (pp. 1227–1237). Boston, MA: Springer. https://doi.org/10.1007/978-1-4419-0211-5_51 .

4. Wu, Z., Wang, X., Chen, Y., Cai, Y., & Deng, J. (2017). Assessing river water quality using water quality index in Lake Taihu Basin, China. Science of the Total Environment, 612, 914–922. https://doi.org/10.1016/J.Scitotenv.2017.08.293 .

5. Bieroza, M. Z., Heathwaite, A. L., Bechmann, M., Kyllmar, K., & Jordan, P. (2018). The concentration-discharge slope as a tool for water quality management. Science of the Total Environment, 630, 738–749. https://doi.org/10.1016/J.Scitotenv.2018.02.256 .

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