The ground level ozone concentration at different locations in Baghdad city was identified. Five
different sites have been chosen to identify the ground level ozone concentration. Al- Dora and Al-
Za'afarania were chosen as areas contained point source ( power plant station ) in addition to high traffic
load , while Al –Uma park, Aden square and Al-Mawal square were chosen as area contained heavy
traffic only (line source). The measurement focuses on spring and fall because these periods display
favorable meteorology to ozone formation. During the research period the maximum values (peaks) for
ground level ozone concentration were observed at fall: at Al-Za'afarania area 101ppb as an average, at
Al-Dora 87 ppb as a

Artificial Neural Network (ANN) model's application is widely increased for wastewater treatment plant (WWTP) variables prediction and forecasting which can enable the operators to take appropriate action and maintaining the norms. It is much easier modeling tool for dealing with complex nature WWTP modeling comparing with other traditional mathematical models. ANN technique significance has been considered at present study for the prediction of sequencing batch reactor (SBR) performance based on effluent's (BOD5/COD) ratio after collecting the required historical daily SBR data for two years operation (2015-2016) from Baghdad Mayoralty and Al-Rustamiya WWTP office, Iraq. The prediction was gotten by the application of a feed-forwa

Were studied some bacteria evidence of pollution as well as the total number of live bacteria in the waters of the Diyala river and selected five stations within the 17 km final Diyala River before its mouth in the Tigris River was the first before the new bridge of the Diyala River about 4 km and the second after the mouth of the water purification plant Rustumiya suit inverselywith temperatures

Chemical pollution is a very important issue that people suffer from and it often affects the nature of health of society and the future of the health of future generations. Consequently, it must be considered in order to discover suitable models and find descriptions to predict the performance of it in the forthcoming years. Chemical pollution data in Iraq take a great scope and manifold sources and kinds, which brands it as Big Data that need to be studied using novel statistical methods. The research object on using Proposed Nonparametric Procedure NP Method to develop an (OCMT) test procedure to estimate parameters of linear regression model with large size of data (Big Data) which comprises many indicators associated with chemi

The objective of all planning research is to plan for human comfort and safety, and one of the most significant natural dangers to which humans are exposed is earthquake risk; therefore, earthquake risks must be anticipated, and with the advancement of global technology, it is possible to obtain information on earthquake hazards. GIS has been utilized extensively in the field of environmental assessment research due to its high potential, and GIS is a crucial application in seismic risk assessment. This paper examines the methodologies used in recent GIS-based seismic risk studies, their primary environmental impacts on urban areas, and the complexity of the relationship between the applied methodological approaches and the resulting env

Intestinal parasites present in freshwater from the Al- Fallujah, Al- Habbaniyah and Al-Alwarar, of the Euphrates river in Iraq are Cryptosporidium spp (25.3%), Giardia sp (3.3%), Eimeria sp (3.3%), Pinworm eggs (3.3%), Naegleria sp (15.3%), Lecane niwati (1.3%), Trichomonas hominis (19.3%), Acanthamoeba spp (24.6%), Entamoeba coli (20.6%), Balantidium coli (12%), Ascaris sp (3.3%), Volvox sp (26%), Chilomastix mesnili (4%), Pelomyxa palustris (2.6%), Trinema enchelys (2.6%), Actinophrys Sol (7.3%), Amobea Vespertilio (9.3%), Rhabditea (5.3%), paramecium bursaria (9.3%), cyst of cestode (6%), Oocyst protozoa (16%), Euglena gracilis (10.6%).were isolated. The study's goal was to isolate some of the parasites that pollute the Euphrat

   The time spent in drilling ahead is usually a significant portion of total well cost. Drilling is an expensive operation including the cost of equipment and material used during the penetration of rock plus crew efforts in order to finish the well without serious problems. Knowing the rate of penetration should help in speculation of the cost and lead to optimize drilling outgoings. Ten wells in the Nasiriya oil field have been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software based on the las files and log record provided. The average rate of penetration and average dynamic elastic propert

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered

Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p

Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data t