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Extraction Drainage Network for Lesser Zab River Basin from DEM using Model Builder in GIS
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ArcHydro is a model developed for building hydrologic information systems to synthesize geospatial and temporal water resources data that support hydrologic modeling and analysis. Raster-based digital elevation models (DEMs) play an important role in distributed hydrologic modeling supported by geographic information systems (GIS). Digital Elevation Model (DEM) data have been used to derive hydrological features, which serve as inputs to various models. Currently, elevation data are available from several major sources and at different spatial resolutions. Detailed delineation of drainage networks is the first step for many natural resource management studies. Compared with interpretation from aerial photographs or topographic maps, automation of drainage network extraction from DEMs is an efficient way and has received considerable attention. This study aims to extract drainage networks from Digital Elevation Model (DEM) for Lesser Zab River Basin. Composition parameters of the drainage network including the numbers of streams and the stream lengths are derived from the DEM beside the delineation of catchment areas in the basin. The results from this application can be used to create input files for many hydrologic models.

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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
Thu Jan 03 2019
Journal Name
International Journal Of Civil Engineering And Technology (ijciet)
Condition Prediction Models of Deteriorated Trunk Sewer Using Multinomial Logistic Regression and Artificial Neural Network
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Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the

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Publication Date
Sat Apr 30 2022
Journal Name
Eastern-european Journal Of Enterprise Technologies
Improvement of noisy images filtered by bilateral process using a multi-scale context aggregation network
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Deep learning has recently received a lot of attention as a feasible solution to a variety of artificial intelligence difficulties. Convolutional neural networks (CNNs) outperform other deep learning architectures in the application of object identification and recognition when compared to other machine learning methods. Speech recognition, pattern analysis, and image identification, all benefit from deep neural networks. When performing image operations on noisy images, such as fog removal or low light enhancement, image processing methods such as filtering or image enhancement are required. The study shows the effect of using Multi-scale deep learning Context Aggregation Network CAN on Bilateral Filtering Approximation (BFA) for d

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Publication Date
Thu Aug 31 2017
Journal Name
Journal Of Engineering
Optimum Dimensions of Hydraulic Structures and Foundation Using Genetic Algorithm coupled with Artificial Neural Network
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      A model using the artificial neural networks and genetic algorithm technique is developed for obtaining optimum dimensions of the foundation length and protections of small hydraulic structures. The procedure involves optimizing an objective function comprising a weighted summation of the state variables. The decision variables considered in the optimization are the upstream and downstream cutoffs lengths and their angles of inclination, the foundation length, and the length of the downstream soil protection. These were obtained for a given maximum difference in head, depth of impervious layer and degree of anisotropy. The optimization carried out is subjected to constraints that ensure a safe structure aga

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Publication Date
Wed Jul 01 2020
Journal Name
Iop Conference Series: Materials Science And Engineering
Predicting the future growth depending on GIS and IDRISI program, city of Najaf-Iraq
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Abstract<p>This study aims to employ modern spatial simulation models to predict the future growth of Al-Najaf city for the year 2036 by studying the change in land use for the time period (1986-2016) because of its importance in shaping future policy for the planning process and decision-making process and ensuring a sustainable urban future, using Geographical information software programs and remote sensing (GIS, IDRISI Selva) as they are appropriate tools for exploring spatial temporal changes from the local level to the global scale. The application of the Markov chain model, which is a popular model that calculates the probability of future change based on the past, and the Cellular Automa</p> ... Show More
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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Application of Artificial Neural Network for Predicting Iron Concentration in the Location of Al-Wahda Water Treatment Plant in Baghdad City
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Iron is one of the abundant elements on earth that is an essential element for humans and may be a troublesome element in water supplies.  In this research an AAN model was developed to predict iron concentrations in the location of Al- Wahda water treatment plant in Baghdad city by water quality assessment of iron concentrations at seven WTPs up stream Tigris River. SPSS software was used to build the ANN model. The input data were iron concentrations in the raw water for the period 2004-2011. The results indicated the best model predicted Iron concentrations at Al-Wahda WTP with a coefficient of determination 0.9142. The model used one hidden layer with two nodes and the testing error was 0.834. The ANN model coul

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Publication Date
Sun Mar 30 2003
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Mathematical Model for Multicomponent Distillation Column
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Publication Date
Thu Jun 27 2019
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
MICROFACIES ANALYSIS AND BASIN DEVELOPMENT OF THE CENOMANIAN - EARLY TURONIAN SEQUENCE IN THE RAFAI, NOOR AND HALFAYA OIL FIELDS, SOUTHEASTERN IRAQ
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    The stratigraphic sequence of Cenomanian-Early Turonian is composed of Ahmadi, Rumaila, and Mishrif formations in the Rifai, Noor and Halfaya Oil Fields within the Mesopotamian Zone of Iraq, which is bounded at top and bottom by unconformity surfaces. The microfacies analysis of the study wells assisted the recognition of five main environments (open marine, basinal, shallow open marine, Rudist biostrome, and lagoon); these microfacies were indicative of a normal lateral change facies from shallow water facies to deeper water and open marine sediments.

 

    Ahmadi Formation (Early Cenomanian) is characterized by open marine sediments during the transgressive conditions, and would be

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Publication Date
Fri Oct 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Forecasting the financial failure of Islamic banks using the Altman model, ---- springate model and ---- Sherrod model / an analytical study in Islamic banks listed on the Iraq Stock Exchange
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Profit is a goal sought by all banks because it brings them income and guarantees them survival and continuity, and on the other hand, facing commitments without financial crisis. Hence the idea of research in his quest to build scientific tools and means that can help bank management in particular, investors, lenders and others to predict financial failure and to detect early financial failures. The research has produced a number of conclusions, the most important of which is that all Islamic banks sample a safe case of financial failure under the Altman model, while according to the Springate model all Islamic banks sample a search for a financial failure except the Islamic Bank of Noor Iraq for Investment and Finance )BINI(. A

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Publication Date
Tue Mar 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
Building a mathematical model for measuring and analyzing the general equilibrium in the Iraqi economy through the IS-lm-BP model
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In order to achieve overall balance in the economy to be achieved in different markets and at one time (market commodity, monetary and labor market and the balance of payments and public budget), did not provide yet a model from which to determine the overall balance in the economy and the difficulty of finding the inter-relationship between all these markets and put them applied in the form of allowing the identification of balance in all markets at once.

One of the best models that have dealt with this subject is a model
(LM-BP-IS), who teaches balance in the commodity market and money market and balance of payments and the importance of this issue This research tries to shed light on the reality

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