Communicating human health risks associated with disinfection by-products in drinking water supplies: a fuzzy-based approach

Sadiq, Rehan; Rodriguez, Manuel J.; Imran, Syed; Najjaran, Homayoun

doi:10.1007/s00477-006-0069-y

Cited by 31 publications

(18 citation statements)

References 27 publications

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“…fuzzy numbers) is an effective method of conducting a risk analysis (Deng et al, 2011). However, communicating uncertainty is an important, yet difficult task, and many different frameworks exist to do so; water quality indices (Sadiq et al, 2007;Van Steenbergen et al, 2012) are one example. Since water resource managers often prefer to use probabilistic measures (rather than possibilistic ones), it is important to convert the possibility of low DO to a comparable probability for effective communication of risk analysis.…”

Section: Risk Analysis Using Defuzzificationmentioning

confidence: 99%

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Khan

Valeo

2016

Hydrol. Earth Syst. Sci.

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Abstract.A new fuzzy neural network method to predict minimum dissolved oxygen (DO) concentration in a highly urbanised riverine environment (in Calgary, Canada) is proposed. The method uses abiotic factors (non-living, physical and chemical attributes) as inputs to the model, since the physical mechanisms governing DO in the river are largely unknown. A new two-step method to construct fuzzy numbers using observations is proposed. Then an existing fuzzy neural network is modified to account for fuzzy number inputs and also uses possibility theory based intervals to train the network. Results demonstrate that the method is particularly well suited to predicting low DO events in the Bow River. Model performance is compared with a fuzzy neural network with crisp inputs, as well as with a traditional neural network. Model output and a defuzzification technique are used to estimate the risk of low DO so that water resource managers can implement strategies to prevent the occurrence of low DO.

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Section: Risk Analysis Using Defuzzificationmentioning

confidence: 99%

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Khan

Valeo

2016

Hydrol. Earth Syst. Sci.

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“…On the other hand, fuzzy logic (FL) method was first developed to explain the human thinking and decision system by Zadeh (1965). Several studies have been carried out using FL in hydrology and water resources planning (Nayak et al 2005;Altunkaynak et al 2005;Ş en and Altunkaynak 2006;Sadiq et al 2006Sadiq et al , 2007Peng and Mayorga 2008;Zarghami and Szidarovszky 2009). Recently, adaptive neuro-fuzzy inference system (ANFIS), which consists of the ANN and FL methods, has been used for prediction stream flow, sediment yield (Nayak et al 2004;Chang and Chang 2006;Chen et al 2006;Firat 2007;Firat and Güngör 2008;Bacanli et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Monthly total sediment forecasting using adaptive neuro fuzzy inference system

Fırat

Güngör

2009

Stoch Environ Res Risk Assess

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Accurate forecasting of sediment is an important issue for reservoir design and water pollution control in rivers and reservoirs. In this study, an adaptive neurofuzzy inference system (ANFIS) approach is used to construct monthly sediment forecasting system. To illustrate the applicability of ANFIS method the Great Menderes basin is chosen as the study area. The models with various input structures are constructed for the purpose of identification of the best structure. The performance of the ANFIS models in training and testing sets are compared with the observed data. To get more accurate evaluation of the results ANFIS models, the best fit model structures are also tested by artificial neural networks (ANN) and multiple linear regression (MLR) methods. The results of three methods are compared, and it is observed that the ANFIS is preferable and can be applied successfully because it provides high accuracy and reliability for forecasting of monthly total sediment.

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“…The risks to human health from chronic arsenic exposure are not all reversible once exposure has ended (note that there are many ways to define and analyze risks in water supply; see Sadiq et al (2007), Lindhe et al (2011) and Petkovic et al (2011) for some examples). Latent health effects, many fatal, may take decades to appear.…”

Section: Introductionmentioning

confidence: 99%

Groundwater arsenic in Bangladesh: what's new for policy research?

Balasubramanya

Horbulyk

2017

Water Policy

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This paper provides an overview of policy responses to arsenic in groundwater in rural Bangladesh to assess their role and potential effectiveness in reducing exposure. With 97% of the country consuming groundwater for drinking, there is a continuing crisis of tens of millions of people exposed to elevated levels of arsenic. An examination of the number of people protected through two major remediation efforts suggests that recent progress may not be sufficient to keep up with the increasing population or to resolve the crisis during this century. Recent developments in remedial options are examined to identify their potential role in an evolving policy and research agenda. There appears to be growing agreement about future research and policy responses that can scale remedial options and make them widely accessible. These include: (1) the need for a reliable and affordable programme of arsenic testing and retesting; (2) attention to risks from other soluble contaminants and pathogens; (3) explicit priority setting across locations, time and to address fairness; and (4) development of value chains to ensure remedial options are supported over time.

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Communicating human health risks associated with disinfection by-products in drinking water supplies: a fuzzy-based approach

Cited by 31 publications

References 27 publications

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Dissolved oxygen prediction using a possibility theory based fuzzy neural network

Monthly total sediment forecasting using adaptive neuro fuzzy inference system

Groundwater arsenic in Bangladesh: what's new for policy research?

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