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Prediction of Ryznar Stability Index for Treated Water of WTPs Located on Al-Karakh Side of Baghdad City using Artificial Neural Network (ANN) Technique
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In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respectively. For Al-Dora WTP, ANN 3 model could be used as R was 92.8%.

 

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Publication Date
Thu Dec 01 2011
Journal Name
Journal Of Engineering
MODELING HOUSEHOLD TRIP GENERATION FOR SELECTED ZONES AT AL-KARKH SIDE OF BAGHDAD CITY
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Trip generation is the first phase in the travel forecasting process. It involves the estimation of the
total number of trips entering or leaving a parcel of land per time period (usually on a daily basis);
as a function of the socioeconomic, locational, and land-use characteristics of the parcel.
The objective of this study is to develop statistical models to predict trips production volumes for a
proper target year. Non-motorized trips are considered in the modeling process. Traditional method
to forecast the trip generation volume according to trip rate, based on family type is proposed in
this study. Families are classified by three characteristics of population social class, income, and
number of vehicle ownersh

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Publication Date
Wed Aug 05 2015
Journal Name
International Journal Of Current Engineering And Technology
Water Quality Index Assessment using GIS Case study: Tigris River in Baghdad City
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In this study water quality index (WQI) was calculated to classify the flowing water in the Tigris River in Baghdad city. GIS was used to develop colored water quality maps indicating the classification of the river for drinking water purposes. Water quality parameters including: Turbidity, pH, Alkalinity, Total hardness, Calcium, Magnesium, Iron, Chloride, Sulfate, Nitrite, Nitrate, Ammonia, Orthophosphate and Total dissolved solids were used for WQI determination. These parameters were recorded at the intakes of the WTPs in Baghdad for the period 2004 to 2011. The results from the annual average WQI analysis classified the Tigris River very poor to polluted at the north of Baghdad (Alkarkh WTP) while it was very poor to very polluted in t

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Publication Date
Tue May 23 2023
Journal Name
Journal Of Engineering
Assessment of Water Quality Index and Water Suitability of the Tigris River for drinking water within Baghdad City, Iraq
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In this study water quality was indicated in terms of Water Quality Index that was determined through summarizing multiple parameters of water test results. This index offers a useful representation of the overall quality of water for public or any intended use as well as indicating pollution, water quality management and decision making. The application of Water Quality Index
(WQI) with sixteen physicochemical water quality parameters was performed to evaluate the quality of Tigris River water for drinking usage. This was done by subjecting the water samples collected from eight stations in Baghdad city during the period 2004-2010 to comprehensive physicochemical analysis. The sixteen physicochemical parameters included: Turbidity, A

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Publication Date
Sat Jun 01 2013
Journal Name
Journal Of Engineering
Assessment of Water Quality Index and Water Suitability of the Tigris River for drinking water within Baghdad City, Iraq
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In this study water quality was indicated in terms of Water Quality Index that was determined through summarizing multiple parameters of water test results. This index offers a useful representation of the overall quality of water for public or any intended use as well as indicating pollution, water quality management and decision making. The application of Water Quality Index (WQI) with sixteen physicochemical water quality parameters was performed to evaluate the quality of Tigris River water for drinking usage. This was done by subjecting the water samples collected from eight stations in Baghdad city during the period 2004-2010 to comprehensive physicochemical analysis. The sixteen physicochemical parameters included: Turbidity,

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Publication Date
Sun Dec 17 2017
Journal Name
Al-khwarizmi Engineering Journal
Experimental and Prediction Using Artificial Neural Network of Bed Porosity and Solid Holdup in Viscous 3-Phase Inverse Fluidization
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In the present investigation, bed porosity and solid holdup in viscous three-phase inverse fluidized bed (TPIFB) are determined for aqueous solutions of carboxy methyl cellulose (CMC) system using polyethylene and polypropylene as  a particles with low-density and diameter (5 mm) in a (9.2 cm) inner diameter with height (200 cm) of vertical perspex column. The effectiveness of gas velocity Ug , liquid velocity UL, liquid viscosity μL, and particle density ρs on bed porosity BP and solid holdups εg were determined. The bed porosity increases with "increasing gas velocity", "liquid velocity", and "liquid viscosity". Solid holdup decreases with increasing gas, liquid

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Publication Date
Fri Feb 01 2019
Journal Name
Environmental Technology & Innovation
The use of Artificial Neural Network (ANN) for modeling of Cu (II) ion removal from aqueous solution by flotation and sorptive flotation process
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Publication Date
Fri Apr 01 2022
Journal Name
Journal Of Engineering
Prediction of Shear Strength Parameters of Gypseous Soil using Artificial Neural Networks
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The shear strength of soil is one of the most important soil properties that should be identified before any foundation design. The presence of gypseous soil exacerbates foundation problems. In this research, an approach to forecasting shear strength parameters of gypseous soils based on basic soil properties was created using Artificial Neural Networks. Two models were built to forecast the cohesion and the angle of internal friction. Nine basic soil properties were used as inputs to both models for they were considered to have the most significant impact on soil shear strength, namely: depth, gypsum content, passing sieve no.200, liquid limit, plastic limit, plasticity index, water content, dry unit weight, and initial

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Publication Date
Sat Jan 01 2022
Journal Name
Methods And Objects Of Chemical Analysis
Spectrophotometric Analysis of Quaternary Drug Mixtures using Artificial Neural network model
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A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twentyfour samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

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Publication Date
Tue Sep 06 2022
Journal Name
Methods And Objects Of Chemical Analysis
Spectrophotometric Analysis of Quaternary Drug Mixtures using Artificial Neural network model
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A Novel artificial neural network (ANN) model was constructed for calibration of a multivariate model for simultaneously quantitative analysis of the quaternary mixture composed of carbamazepine, carvedilol, diazepam, and furosemide. An eighty-four mixing formula where prepared and analyzed spectrophotometrically. Each analyte was formulated in six samples at different concentrations thus twenty four samples for the four analytes were tested. A neural network of 10 hidden neurons was capable to fit data 100%. The suggested model can be applied for the quantitative chemical analysis for the proposed quaternary mixture.

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Publication Date
Sun Jan 27 2019
Journal Name
Civil Engineering Journal
Prediction of Sediment Accumulation Model for Trunk Sewer Using Multiple Linear Regression and Neural Network Techniques
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Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers propos

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