The objective of this research is to select the most suitable drilling location of new groundwater exploration wells, with a decision-making tool from Geographic Information System (GIS). The optimum location will be evaluated base on the hydro-geoelectrical parameter derived from Vertical Electrical Sound (VES) including Longitudinal Conductance, the thickness of the aquifer, the apparent resistivity and Transmissivity. From the Geo-electrical method (VES) the finds shows that the aquifers in the study area have Apparent Resistivity ranging from 0.32 to 40.24 Ωm, Thickness between 0.21 m to 15.06 m, Longitudinal Conductance ranging from 0.006 to 10.246Ω^-1 and Transmissivity ranging from 0.14 to

The estimation of the initial oil in place is a crucial topic in the period of exploration, appraisal, and development of the reservoir. In the current work, two conventional methods were used to determine the Initial Oil in Place. These two methods are a volumetric method and a reservoir simulation method. Moreover, each method requires a type of data whereet al the volumetric method depends on geological, core, well log and petrophysical properties data while the reservoir simulation method also needs capillary pressure versus water saturation, fluid production and static pressure data for all active wells at the Mishrif reservoir. The petrophysical properties for the studied reservoir is calculated using neural network technique

This study discussed a biased estimator of the Negative Binomial Regression model known as (Liu Estimator), This estimate was used to reduce variance and overcome the problem Multicollinearity between explanatory variables, Some estimates were used such as Ridge Regression and Maximum Likelihood Estimators, This research aims at the theoretical comparisons between the new estimator (Liu Estimator) and the estimators

The study was conducted during spring seasons of 2000 and 2001.The aim was to study the changes in the moisture content of sunflower plants during growth stages under hardening conditions to drought tolerance .Agricultural practices were made according to recommendation. Asplit-split plots design was used with three replications. The main plots included irrigation treatments:irrigation to100%(full irrigation),75and50%of available water. The sub plots were the cultivars Euroflor and Flame.The sub-sub plots represented four seed soaking treatments: Control (unsoaked), soaking in water ,Paclobutrazol solution(250ppm),and Pix solution(500ppm). The soaking continued for 24 hours then seeds were dried at room temperature until they regained t

The process of recharge the groundwater is the key factors that need to be addressed as is the foundation upon which the process of groundwater use and management properly, especially in arid and semi-arid, like Iraq, so the values of groundwater recharge calculated vary depending on the method used in the calculation and the more factors calculated in the process of calculating the values of groundwater recharge increased margin of error in these values. In this study were selected four methods to calculate the value of recharge for groundwater (fluctuating water table, water balance of the basin, numerical modeling, and balance of chloride ion mass in unsaturated zone) was applied in the Plateau area of Karbala (Dibdiba formation) has

Most of the Weibull models studied in the literature were appropriate for modelling a continuous random variable which assumes the variable takes on real values over the interval [0,∞]. One of the new studies in statistics is when the variables take on discrete values. The idea was first introduced by Nakagawa and Osaki, as they introduced discrete Weibull distribution with two shape parameters q and β where      0 < q < 1 and b > 0. Weibull models for modelling discrete random variables assume only non-negative integer values. Such models are useful for modelling for example; the number of cycles to failure when components are subjected to cyclical loading. Discrete Weibull models can be obta

المتغير العشوائي X  له توزيع أسي اذا كان له دالة احتمالية الكثافة بالشكل:

عندما  ، هذه هي الحالة الخاصة لتوزيع كاما.

غالباً جداً ولسبب معقول تأخذ . الحالة الخاصة لـ (1) التي نحصل عليها تسمى بالتوزيع الاسي لمعلمة واحدة.

اذا كانت  ، ، التوزيع في هذه الحالة يسمى التوزيع الاسي القياسي

اما بالنسب

Weibull Distribution is one of most important distribution and it is mainly used in reliability and in distribution of life time. The study handled two parameter and three-parameter Weibull Distribution in addition to five –parameter Bi-Weibull distribution. The latter being very new and was not mentioned before in many of the previous references. This distribution depends on both the two parameter and the three –parameter Weibull distributions by using the scale parameter (α) and the shape parameter (b) in the first and adding the location parameter (g)to the second and then joining them together to produce a distribution with five parameters.

In this paper a new idea was introduced which is finding a new distribution from other distributions using mixing parameters; w_i  where  0 < w_i < 1 ­and . Therefore we can get many mixture distributions with a number of parameters. In this paper I introduced the idea of a mixture Weibull distribution which is produced from mixing two Weibull distributions; the first with two parameters, the scale parameter , and the shape parameter,  and the second also has the scale parameter , and the shape parameter,  in addition to the location parameter, . These two distributions were mixed using a new parameter which is the mixing parameter w which represents the proportion

Weibull Distribution is one of most important distribution and it is mainly used in reliability and in distribution of life time. The study handled two parameter and three-parameter Weibull Distribution in addition to five –parameter Bi-Weibull distribution. The latter being very new and was not mentioned before in many of the previous references. This distribution depends on both the two parameter and the three –parameter Weibull distributions by using the scale parameter (α) and the shape parameter (b) in the first and adding the location parameter (g)to the second and then joining them together to produce a distribution with five parameters.