Literature DB >> 18776610

Assessment of the groundwater salinity monitoring network of the Tehran region: application of the discrete entropy theory.

F Masoumi1, R Kerachian.   

Abstract

In this paper, a new entropy-based approach is developed for assessing the location of salinity monitoring stations in the Tehran Aquifer, Tehran, Iran. To find the optimal distance among stations, the measure of Transinformation in the Entropy Theory is used. Then a Transinformation-Distance (T-D) curve is developed and used in a multi-objective GA-based optimization model, which provides the best locations for monitoring stations. Because of the large area of the Tehran aquifer and significant spatial variations of the Electrical Conductivity (EC) of the groundwater in the study area, the C-means clustering method is used to classify the study area to some homogenous zones. The optimization model is applied to each zone to find the optimal location of monitoring stations. The results show the applicability and the efficiency of the model in assessing the groundwater monitoring systems. Copyright IWA Publishing 2008.

Mesh:

Year:  2008        PMID: 18776610     DOI: 10.2166/wst.2008.674

Source DB:  PubMed          Journal:  Water Sci Technol        ISSN: 0273-1223            Impact factor:   1.915


  3 in total

1.  Revising river water quality monitoring networks using discrete entropy theory: the Jajrood River experience.

Authors:  Najmeh Mahjouri; Reza Kerachian
Journal:  Environ Monit Assess       Date:  2010-05-25       Impact factor: 2.513

2.  Optimal redesign of groundwater quality monitoring networks: a case study.

Authors:  Fariborz Masoumi; Reza Kerachian
Journal:  Environ Monit Assess       Date:  2009-02-06       Impact factor: 2.513

3.  Evaluating the main sources of groundwater pollution in the southern Tehran aquifer using principal component factor analysis.

Authors:  Hooman Ghahremanzadeh; Roohollah Noori; Akbar Baghvand; Touraj Nasrabadi
Journal:  Environ Geochem Health       Date:  2017-12-16       Impact factor: 4.609

  3 in total

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