Literature DB >> 19674829

An imprecise fuzzy risk approach for water quality management of a river system.

S Rehana1, P P Mujumdar.   

Abstract

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max-min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga-Bhadra river system in India.

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Year:  2009        PMID: 19674829     DOI: 10.1016/j.jenvman.2009.07.007

Source DB:  PubMed          Journal:  J Environ Manage        ISSN: 0301-4797            Impact factor:   6.789


  5 in total

1.  Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study.

Authors:  Mohammad Reza Nikoo; Reza Kerachian; Akbar Karimi; Ali Asghar Azadnia
Journal:  Environ Monit Assess       Date:  2012-07-10       Impact factor: 2.513

2.  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

3.  Impact of climate change on river water temperature and dissolved oxygen: Indian riverine thermal regimes.

Authors:  M Rajesh; S Rehana
Journal:  Sci Rep       Date:  2022-06-02       Impact factor: 4.996

4.  An integrated fuzzy-based advanced eutrophication simulation model to develop the best management scenarios for a river basin.

Authors:  Rallapalli Srinivas; Ajit Pratap Singh
Journal:  Environ Sci Pollut Res Int       Date:  2018-01-14       Impact factor: 4.223

5.  Detecting SARS-CoV-2 RNA prone clusters in a municipal wastewater network using fuzzy-Bayesian optimization model to facilitate wastewater-based epidemiology.

Authors:  Srinivas Rallapalli; Shubham Aggarwal; Ajit Pratap Singh
Journal:  Sci Total Environ       Date:  2021-03-08       Impact factor: 7.963

  5 in total

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