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A Multi-variables Multi -sites Model for Forecasting Hydrological Data Series
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A multivariate multisite hydrological data forecasting model was derived and checked using a case study. The philosophy is to use simultaneously the cross-variable correlations, cross-site correlations and the time lag correlations. The case study is of two variables, three sites, the variables are the monthly rainfall and evaporation; the sites are Sulaimania, Dokan, and Darbandikhan.. The model form is similar to the first order auto regressive model, but in matrices form. A matrix for the different relative correlations mentioned above and another for their relative residuals were derived and used as the model parameters. A mathematical filter was used for both matrices to obtain the elements. The application of this model indicates it's capability of preserving the statistical characteristics of the observed series. The preservation was checked by using (t-test) and (F-test) for the monthly means and variances which gives 98.6% success for means and 81% success for variances. Moreover for the same data two well-known models were used for the sake of comparison with the developed model. The single-site singlevariable auto regressive first order and the multi-variable single-site models. The results of the three models were compared using (Akike test) which indicates that the developed model is more successful ,since it gave minimum (AIC) value for Sulaimania rainfall, Darbandikhan rainfall, and Darbandikhan evaporation, while Matalas model gave minimum (AIC) value for Sulaimania evaporation and Dokan rainfall, and Markov AR (1) model gave minimum (AIC) value for only Dokan evaporation).However, for these last cases the (AIC) given by the developed model is slightly greater than the minimum corresponding value.

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Publication Date
Tue Dec 16 2025
Journal Name
Cab Reviews: Perspectives In Agriculture, Veterinary Science, Nutrition And Natural Resources
From data to decision: How wearable plant sensors help improving proactive irrigation strategies and water use efficiency
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Wearable sensors are a revolutionary tool in agriculture because they collect accurate data on plant environmental conditions that affect plant growth in real-time. Moreover, this technology is crucial in increasing agricultural sustainability and productivity by improving irrigation strategies and water resource management. This review examines the role of wearable sensors in measuring plant water content, leaf and air humidity, stem flow, plant and air temperature, light, and soil moisture sensors. Wearable sensors are designed to monitor various plant physiological parameters in real-time. These data, obtained through wearable sensors, provide information on plant water use and physiology, making our agricultural choices more informed an

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Publication Date
Wed Jan 01 2014
Journal Name
Journal Of Engineering Geology And Hydrogeology
Assessing Pullout Resistance of Earth Reinforced Embankment Model
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- The sandy soil with high gypsum content (usually referred to as gypseous soil) covers vast area in south, east, middle and west regions of Iraq, such soil possess a type of cohesive forces when attached with optimum amount of water, then compacted and allowed to cure, but losses its strength when flooded with water again. Much work on earth reinforcement was published which concentrate on the gain in bearing capacity in the reinforced layer using different types of cohesive or cohesion less soil and various types of reinforcement such as plastic, metal, grids, and synthetic textile. Little attention was paid to there enforce gypseous soil. The objective of this work is to study the interaction between such soil and reinforcement strips

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Publication Date
Tue Oct 19 2021
Journal Name
International Journal Of Online And Biomedical Engineering (ijoe)
Object Tracking Using Adaptive Diffusion Flow Active Model
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Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this

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Publication Date
Thu Jan 21 2021
Journal Name
Recent Innovations In Chemical Engineering (formerly Recent Patents On Chemical Engineering)
Soap Production Using Vacuum Reactive Distillation: Batch Model
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Introduction: Although soap industry is known from hundreds of years, the development accompanied with this industry was little. The development implied the mechanical equipment and the additive materials necessary to produce soap with the best specifications of shape, physical and chemical properties. Objectives: This research studies the use of vacuum reactive distillation VRD technique for soap production. Methods: Olein and Palmitin in the ratio of 3 to 1 were mixed in a flask with NaOH solution in stoichiometric amount under different vacuum pressures from -0.35 to -0.5 bar. Total conversion was reached by using the VRD technique. The soap produced by the VRD method was compared with soap prepared by the reaction - only method which

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Publication Date
Fri Sep 01 2017
Journal Name
Global Journal Of Pure And Applied Mathematics
Solution of Modified Kuznetsov Model with Mixed Therapy
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In this paper two modifications on Kuznetsov model namely on growth rate law and fractional cell kill term are given. Laplace Adomian decomposition method is used to get the solution (volume of the tumor) as a function of time .Stability analysis is applied. For lung cancer the tumor will continue in growing in spite of the treatment.

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Publication Date
Mon Feb 01 2021
Journal Name
Journal Of Physics: Conference Series
Bayesian Computational Methods of the Logistic Regression Model
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Abstract<p>In this paper, we will discuss the performance of Bayesian computational approaches for estimating the parameters of a Logistic Regression model. Markov Chain Monte Carlo (MCMC) algorithms was the base estimation procedure. We present two algorithms: Random Walk Metropolis (RWM) and Hamiltonian Monte Carlo (HMC). We also applied these approaches to a real data set.</p>
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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Modified Mathematical Model of Tumor Treatment by Radiotherapy
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In this research, a mathematical model of tumor treatment by radiotherapy is studied and a new modification for the model is proposed as well as introducing the check for the suggested modification. Also the stability of the modified model is analyzed in the last section.

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Publication Date
Mon Jan 01 2024
Journal Name
Journal Of Engineering
Face-based Gender Classification Using Deep Learning Model
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Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

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Publication Date
Fri Aug 30 2024
Journal Name
Iraqi Journal Of Science
The Migration Effect on an Eco-toxicant Model
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This paper proposes and studies an ecotoxicant system with Lotka-Volterra functional response for predation including prey protective region. The equilibrium points and the stability of this model have been investigated analytically both locally and globally. Finally, numerical simulations and graphical representations have been utilized to support our analytical findings

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Publication Date
Fri Jan 31 2020
Journal Name
Iraqi Geological Journal
GEOLOGICAL MODEL OF MAUDDUD FORMATION IN BADRA OILFIELD
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A 2D geological model for Mauddud Formation in the Badra oil field is built using Rockworks 16 software. Mauddud Formation produces oil from limestone units; it represents the main reservoir in the Badra oil field. Six wells (BD-1, BD-2, BD-4, BD-5, P-15, and P-19) are selected to build facies and petrophysical (Porosity and Water saturation) models. Wells data are taken from the core and cutting samples and studied through the microscopic. The petrophysical data (effective porosity and water saturation) are derived from computer processes interpretation results that are calculated by using Interactive Petrophysics software. The 2D models are built to illustrate the vertical and horizontal distribution of petrophysical properties between we

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