2019
DOI: 10.23851/mjs.v29i3.617
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Fuzzy Logic Inference Index to Assess the Water Quality of Tigris River within Baghdad City

Abstract: This study aimed to develop a new water quality index for routine assessment of the river water quality for drinking purpose based on fuzzy logic artificial intelligence method. Four water quality parameters were involved in light of their significance to Iraqi waters, these parameters are biological oxygen demand, and total dissolved solids, total hardness, and fecal coliform. Fuzzy logic inference system with specific rules was developed by Matlab software using Mamdani fuzzy logic Max–Min inference system m… Show more

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“…Numerous research using the FWQI approach have been published during the past few decades: FWQI in Ribeira do Iguape River (2009) (Lermontov et al, 2009); WQI development using fuzzy logic in Karoon River (2011) (Babaei Semirom et al, 2011); water quality management ((Che Osmi et al, 2016) Water quality classification( (Dewanti & Abadi, 2019); River-Pollution Decision Support Expert System (Nasiri et al, n.d.); 3 fuzzy water pollution index in Qu River (2016) (Li et al, 2016); application of adaptive neuro-fuzzy to estimate BOD of Surma River (Ahmed & Shah, 2017). Fuzzy logic inference index in Tigris River (2019) (Ewaid et al, 2019;Li et al, 2016;Quiñones-Huatangari et al, 2020); WQI using fuzzy logic in Utcubamba River (2020) (Quiñones-Huatangari et al, 2020). This was the first water quality modelling study on the Narmada River.…”
Section: Introductionmentioning
confidence: 99%
“…Numerous research using the FWQI approach have been published during the past few decades: FWQI in Ribeira do Iguape River (2009) (Lermontov et al, 2009); WQI development using fuzzy logic in Karoon River (2011) (Babaei Semirom et al, 2011); water quality management ((Che Osmi et al, 2016) Water quality classification( (Dewanti & Abadi, 2019); River-Pollution Decision Support Expert System (Nasiri et al, n.d.); 3 fuzzy water pollution index in Qu River (2016) (Li et al, 2016); application of adaptive neuro-fuzzy to estimate BOD of Surma River (Ahmed & Shah, 2017). Fuzzy logic inference index in Tigris River (2019) (Ewaid et al, 2019;Li et al, 2016;Quiñones-Huatangari et al, 2020); WQI using fuzzy logic in Utcubamba River (2020) (Quiñones-Huatangari et al, 2020). This was the first water quality modelling study on the Narmada River.…”
Section: Introductionmentioning
confidence: 99%