Bayesian deduction for redundancy detection in groundwater quality monitoring networks-Reference-Cited by-同舟云学术

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Bayesian deduction for redundancy detection in groundwater quality monitoring networks

Published:2008-08 Issue:8 Volume:44 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Ammar Khalil¹,Khalil Abedalrazq²,McKee Mac³,Kaluarachchi Jagath¹

Affiliation:

1. Department of Civil and Environmental Engineering; Utah State University; Logan Utah USA

2. Department of Earth and Environmental Engineering; Columbia University; New York New York USA

3. Utah Water Research Laboratory; Utah State University; Logan Utah USA

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1029/2006WR005616/fullpdf

Reference19 articles.

1. Support vectors-based groundwater head observation networks design;Asefa;Water Resour. Res.,2004

2. Support vector machines (SVMs) for monitoring network design;Asefa;Ground Water,2005

3. Multiobjective particle swarm optimization for parameter estimation in hydrology;Gill;Water Resour. Res.,2006

4. Water Quality Monitoring Network Design

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1. A novel groundwater monitoring network design framework for long-term and economical data monitoring;Groundwater for Sustainable Development;2024-08

2. Exact optimisation of spatiotemporal monitoring networks by p‐splines with applications in groundwater assessment;Environmetrics;2024-06-28

3. Groundwater level monitoring network design with machine learning methods;Journal of Hydrology;2023-10

4. Geostatistical based optimization of groundwater monitoring well network design;Frontiers in Earth Science;2023-09-06

5. Relevance vector machine (RVM);Handbook of Hydroinformatics;2023

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