This research represents a practical attempt applied to calibrate and verify a hydraulic model for the Blue Nile River. The calibration procedures are performed using the observed data for a previous period and comparing them with the calibration results while verification requirements are achieved with the application of the observed data for another future period and comparing them with the verification results. The study objective covered a relationship of the river terrain with the distance between the assumed points of the dam failures along the river length. The computed model values and the observed data should conform to the theoretical analysis and the overall verification performance of the model by comparing it with anothe

This research represents a practical attempt applied to calibrate and verify a hydraulic model for the Blue Nile River. The calibration procedures are performed using the observed data for a previous period and comparing them with the calibration results while verification requirements are achieved with the application of the observed data for another future period and comparing them with the verification results. The study objective covered a relationship of the river terrain with the distance between the assumed points of the dam failures along the river length. The computed model values and the observed data should conform to the theoretical analysis and the overall verification performance of the model by comparing i

Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

 The logistic regression model of the most important regression models a non-linear which aim getting estimators have a high of efficiency, taking character more advanced in the process of statistical analysis for being a models appropriate form of Binary Data.                                                          

Among the problems that appear as a result of the use of some statistical methods I

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

The study included the investigation of fungi ringed and inventory and Aflatoxins in rice and recorded average temperatures and humidity 22.75 degree Celsius and 13.2% respectively were obtained 1356 isolation innate possible diagnosis 15 species inherent in rice imported back to 8 races represented races b Fusarium , Cladosporium, Aspergillus and Alternaria

A seemingly uncorrelated regression (SUR) model is a special case of multivariate models, in which the error terms in these equations are contemporaneously related. The method estimator (GLS) is efficient because it takes into account the covariance structure of errors, but it is also very sensitive to outliers. The robust SUR estimator can dealing outliers. We propose two robust methods for calculating the estimator, which are (S-Estimations, and FastSUR). We find that it significantly improved the quality of SUR model estimates. In addition, the results gave the FastSUR method superiority over the S method in dealing with outliers contained in the data set, as it has lower (MSE and RMSE) and higher (R-Squared and R-Square Adjus