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Artificial Neural Networks Modeling of Total Dissolved Solid in the Selected Locations on Tigris River, Iraq
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The study aims to predict Total Dissolved Solids (TDS) as a water quality indicator parameter at spatial and temporal distribution of the Tigris River, Iraq by using Artificial Neural Network (ANN) model. This study was conducted on this river between Mosul and Amarah in Iraq on five positions stretching along the river for the period from 2001to 2011. In the ANNs model calibration, a computer program of multiple linear regressions is used to obtain a set of coefficient for a linear model. The input parameters of the ANNs model were the discharge of the Tigris River, the year, the month and the distance of the sampling stations from upstream of the river. The sensitivity analysis indicated that the distance and discharge have the most significant affect on the predicted TDS concentrations. The results showed that a network with (8) hidden neurons was highly accurate in predicting TDS concentration. The correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE) between measured data and model outputs were calculated as 0.975, 113.9 and 11.51%, respectively for testing data sets. Comparisons between final results of ANNs and multiple linear regressions (MLR) showed that the ANNs model could be successfully applied and provides high accuracy to predict TDS concentrations as a water quality parameter.

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Publication Date
Thu Feb 29 2024
Journal Name
International Journal Of Design & Nature And Ecodynamics
Artificial Neural Network Assessment of Groundwater Quality for Agricultural Use in Babylon City: An Evaluation of Salinity and Ionic Composition
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Publication Date
Thu Jun 20 2024
Journal Name
Fizjoterapia Polska
Development Artificial Neural Network (ANN) computing model to analyses men's 100¬meter sprint performance trends
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Coaches and analysts face a significant challenge of inaccurate estimation when analyzing Men's 100 Meter Sprint Performance, particularly when there is limited data available. This necessitates the use of modern technologies to address the problem of inaccurate estimation. Unfortunately, current methods used to estimate Men's 100 Meter Sprint Performance indexes in Iraq are ineffective, highlighting the need to adopt new and advanced technologies that are fast, accurate, and flexible. Therefore, the objective of this study was to utilize an advanced method known as artificial neural networks to estimate four key indexes: Accelerate First of 10 meters, Speed Rate, Time First of 10 meters, and Reaction Time. The application of artifi

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Publication Date
Thu Nov 21 2019
Journal Name
Journal Of Engineering
A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model
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In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

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Publication Date
Fri Sep 30 2022
Journal Name
Iraqi Geological Journal
Biostratigraphy of the Late Cretaceous-Early Paleocene Succession in Selected Wells, Jambur Oil field, Kirkuk, Northern Iraq
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The Late Cretaceous-Early Paleocene Shiranish and Aliji formations have been studied in three selected wells in Jambur Oil Field (Ja-50, Ja-53, and Ja-67) in Kirkuk, Northeastern Iraq. This study included lithostratigraphy and biostratigraphy. The Late Campanian-Maastrichtian Shiranish Formation consist mainly of thin marly and chalky limestone beds overlain by thin marl beds, with some beds of marly limestone representing an outer shelf basinal environment, the unconformable contact with the above Middle Paleocene-Early Eocene Aliji Formation contain layers of limestone with marly limestone and chalky limestone which represents an outer shelf basinal environment. Five Biozones in the Shiranish Formation were determined which are: 1

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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Engineering
Artificial Neural Network (ANN) for Prediction of Viscosity Reduction of Heavy Crude Oil using Different Organic Solvents
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The increase globally fossil fuel consumption as it represents the main source of energy around the world, and the sources of heavy oil more than light, different techniques were used to reduce the viscosity and increase mobility of heavy crude oil. this study focusing on the experimental tests  and modeling with Back Feed Forward Artificial Neural Network (BFF-ANN) of the dilution technique to reduce a  heavy oil viscosity that was collected from the south- Iraq oil fields using organic solvents, organic diluents with different weight percentage  (5, 10 and  20 wt.% )  of  (n-heptane, toluene, and a mixture of  different ratio

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Publication Date
Fri Apr 30 2021
Journal Name
Eastern-european Journal Of Enterprise Technologies
Implementation of artificial neural network to achieve speed control and power saving of a belt conveyor system
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According to the importance of the conveyor systems in various industrial and service lines, it is very desirable to make these systems as efficient as possible in their work. In this paper, the speed of a conveyor belt (which is in our study a part of an integrated training robotic system) is controlled using one of the artificial intelligence methods, which is the Artificial Neural Network (ANN). A visions sensor will be responsible for gathering information about the status of the conveyor belt and parts over it, where, according to this information, an intelligent decision about the belt speed will be taken by the ANN controller. ANN will control the alteration in speed in a way that gives the optimized energy efficiency through

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Publication Date
Thu Dec 21 2017
Journal Name
International Journal Of Engineering & Technology
Determination of liquefaction potential for two selected sites in Kerbala city- middle of Iraq
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Geotechnical characterization of the sites has been investigated with the collection of borehole data from different sources. Using the data, grain size distribution curves have been developed to understand the particle size distribution of the alluvium present. These curves were further used for preliminary assessment of liquefiable areas. From geotechnical characterization, it has been observed that the soil profile in the two sites is dominated by sand and silty sand.Seed and Idriss (1971) approachhas been usedevaluatethe liquefaction potentialbydeterminationof the relation between the maximum ground acceleration (a max/g) valuesdue to an earthquake and the relative density of a sand deposit in the field. The results reveal that

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Publication Date
Thu Feb 01 2024
Journal Name
Iop Conference Series: Earth And Environmental Science
Processing and re-interpretation of gravity bouguer map of a selected area in the W-NW of Iraq
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Abstract<p>Geophysical data interpretation is crucial in characterizing the subsurface structure. The Bouguer gravity map analysis of the W-NW region of Iraq serves as the basis for the current geophysical research. The Bouguer gravity data were processed using the Power Spectrum Analysis method. Four depth slices have been acquired after the PSA process, which are: 390 m, 1300 m, 3040 m, and 12600 m depth. The gravity anomaly depth maps show that shallow-depth anomalies are mainly related to the sedimentary cover layers and structures, while the gravity anomaly of the deeper depth slice of 12600 m is more presented to the basement rocks and mantle uplift. The 2D modeling technique was used for </p> ... Show More
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Publication Date
Sun May 01 2016
Journal Name
Journal Of Engineering
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% respe

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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
Pollution Bio- indicators In Al-Diwania River A1- (^a?lisia Province / Iraq
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Benthic invertebrates were used as bio- indicators to evaluate the pollution in -Diwania River . Five stations were selected for this purpose , extending from A1 -?? rtream to A1- Sadeer District downstream . The percentage of?ct uP?str?^ ? ?, oligochaeta to total benthic invertebrates were calculated . The population density of evaluation. 'I'he results Were ??? Tubificid worms without hair ehaetae was ©iso used IOBS(01igochaete Index of Sediment Bioindicati©n ), TUSP ? presented as indices Io (Tubificidae Species Percentage ) & degree of pollution Eo . IT was noticed that the 0 in??37.17 percentage of ©lig©chaeta to the total benthic invertebrates ranged between to 60.685 in station 3 , while the percentage ©f Tubificid w©rms t© ©

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