Literature DB >> 16678900

Assessing water quality in rivers with fuzzy inference systems: a case study.

William Ocampo-Duque1, Núria Ferré-Huguet, José L Domingo, Marta Schuhmacher.   

Abstract

In recent years, fuzzy-logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental problems. In the present study, a methodology based on fuzzy inference systems (FIS) to assess water quality is proposed. A water quality index calculated with fuzzy reasoning has been developed. The relative importance of water quality indicators involved in the fuzzy inference process has been dealt with a multi-attribute decision-aiding method. The potential application of the fuzzy index has been tested with a case study. A data set collected from the Ebro River (Spain) by two different environmental protection agencies has been used. The current findings, managed within a geographic information system, clearly agree with official reports and expert opinions about the pollution problems in the studied area. Therefore, this methodology emerges as a suitable and alternative tool to be used in developing effective water management plans.

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Year:  2006        PMID: 16678900     DOI: 10.1016/j.envint.2006.03.009

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  17 in total

1.  A concurrent neuro-fuzzy inference system for screening the ecological risk in rivers.

Authors:  William Ocampo-Duque; Ronnie Juraske; Vikas Kumar; Martí Nadal; José Luis Domingo; Marta Schuhmacher
Journal:  Environ Sci Pollut Res Int       Date:  2012-04-29       Impact factor: 4.223

2.  A probabilistic water quality index for river water quality assessment: a case study.

Authors:  Mohammad Reza Nikoo; Reza Kerachian; Siamak Malakpour-Estalaki; Seyyed Nasser Bashi-Azghadi; Mohammad Mahdi Azimi-Ghadikolaee
Journal:  Environ Monit Assess       Date:  2010-12-29       Impact factor: 2.513

Review 3.  Development of river water quality indices-a review.

Authors:  Arief Dhany Sutadian; Nitin Muttil; Abdullah Gokhan Yilmaz; B J C Perera
Journal:  Environ Monit Assess       Date:  2015-12-28       Impact factor: 2.513

4.  Application of physicochemical data for water-quality assessment of watercourses in the Gdansk Municipality (South Baltic coast).

Authors:  Monika Cieszynska; Marek Wesolowski; Maria Bartoszewicz; Malgorzata Michalska; Jacek Nowacki
Journal:  Environ Monit Assess       Date:  2011-06-08       Impact factor: 2.513

5.  Waste load allocation in rivers under uncertainty: application of social choice procedures.

Authors:  Najmeh Mahjouri; Mohammad-Reza Abbasi
Journal:  Environ Monit Assess       Date:  2015-01-22       Impact factor: 2.513

6.  Assessment for water quality by artificial neural network in Daya Bay, South China Sea.

Authors:  Mei-Lin Wu; You-Shao Wang; Ji-Dong Gu
Journal:  Ecotoxicology       Date:  2015-04-07       Impact factor: 2.823

7.  Dynamic water quality evaluation based on fuzzy matter-element model and functional data analysis, a case study in Poyang Lake.

Authors:  Bing Li; Guishan Yang; Rongrong Wan; Georg Hörmann
Journal:  Environ Sci Pollut Res Int       Date:  2017-06-28       Impact factor: 4.223

8.  Anthropogenic activities impact on atmospheric environmental quality in a gas-flaring community: application of fuzzy logic modelling concept.

Authors:  Olayiwola Akin Akintola; Abimbola Yisau Sangodoyin; Foluso Oyedotun Agunbiade
Journal:  Environ Sci Pollut Res Int       Date:  2018-05-24       Impact factor: 4.223

9.  A real-time, dynamic early-warning model based on uncertainty analysis and risk assessment for sudden water pollution accidents.

Authors:  Dibo Hou; Xiaofan Ge; Pingjie Huang; Guangxin Zhang; Hugo Loáiciga
Journal:  Environ Sci Pollut Res Int       Date:  2014-05-01       Impact factor: 4.223

10.  Water quality time series for Big Melen stream (Turkey): its decomposition analysis and comparison to upstream.

Authors:  N Karakaya; F Evrendilek
Journal:  Environ Monit Assess       Date:  2009-05-12       Impact factor: 2.513

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