A review of Machine learning models and parameters for groundwater issues-Reference-Cited by-同舟云学术

A review of Machine learning models and parameters for groundwater issues

Published:2023-05-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 6th International Conference on Networking, Intelligent Systems & Security
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Author:

El Ansari Rachid¹^ORCID,El Bouhadioui Mohamed²^ORCID,Aboutafail Moulay othman³^ORCID,Mejjad Nezha⁴^ORCID,Jamil Hicham⁵^ORCID,Jamal Elhassan⁶^ORCID,Rissouni Youssef⁶^ORCID,Zouiten Mohammed⁷^ORCID,Boutracheh Hicham⁸^ORCID,Moumen Aniss⁸^ORCID

Affiliation:

1. Laboratory : Engineering Sciences, National School of Applied Sciences, Kenitra,Morocco, Morocco

2. National school of Mines, Rabat, Morocco, Morocco

3. Laboratory : Engineering Sciences, National School of Applied Sciences,Kenitra,Morocco, Morocco

4. National Centre for Nuclear Energy,Science and Technology, RABAT,Morocco, Morocco

5. Natural Resources Geosciences Laboratory, Faculty of Sciences, Kenitra,Morocco, Morocco

6. Geosciences Laboratory, Faculty of Sciences,Kenitra,Morocco, Morocco

7. Geography Department, Faculty Polydisciplinary,Taza,Morocco, Morocco

8. Laboratory: Engineering Sciences, National School of Applied Sciences , Kenitra,Morocco, Morocco

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3607720.3607777

Reference27 articles.

1. Big Data Analytic Architecture for Water Resources Management

2. Modeling and Uncertainty Analysis of Groundwater Level Using Six Evolutionary Optimization Algorithms Hybridized with ANFIS, SVM, and ANN

3. Coupling of Numerical Flow Model with the Susceptibility Index Method (SI) to Assess the Groundwater Vulnerability to Pollution

4. Groundwater Level Modeling with Machine Learning: A Systematic Review and Meta-Analysis

5. Changes in global groundwater organic carbon driven by climate change and urbanization

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