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Predicting Potential Salinity in River Water for Irrigation Water Purposes Using Integrative Machine Learning Models
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ABSTRACT<p>Accurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector functional link (dRVFL), general regression neural network (GRNN), multivariate adaptive regression spline (MARS), online sequential extreme learning machine (OSELM) and extreme gradient boosting decision tree (XGBoost) when compared with observed river salinity data. Also, the KELM‐BSSADE model effectively identified optimal inputs through the Boruta‐XGBoost (B‐XGB) feature selection method. Four metaheuristic‐based KELM models were developed, utilizing grey wolf optimizer, whale optimization, slime mould algorithm and equilibrium optimizer, further illustrating the capability of KELM‐BSSADE in estimating potential salinity in river water. By accurately estimating potential salinity, KELM‐BSSADE can assist in optimizing irrigation practices, ensuring that agricultural demands are met while minimizing the risk of salinity‐related crop damage.</p>
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Publication Date
Mon Mar 08 2021
Journal Name
Baghdad Science Journal
Benthic Algae in Lower Zab Tributary and Tigris river
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Benthic algae of Tigris river and one of its northern tributary the lower Zab were study at monthly intervals during Nov. 2001-Oct. 2002. Four sites were selected, a total of 115 species of algae were identified during this study, diatoms was the dominating group (86 species) followed by Chlorophyta (18 species), Cyanophyta (7species), Euglenophyta (2 species) and one species for each of Pyrrophyta and Chryzophyta. Pennate diatoms formed the major density within the identified algae and distributed among all stations especially the species Achnanthes minutissima, Navicula gracilis and Nitzschia palea, the diatoms bloomed in spring and autumn seasons. Bio-diversity and density of benthic algae in Tigris river was affected negatively by the e

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Publication Date
Sat Apr 01 2023
Journal Name
Journal Of Engineering
Minimum Instream Environmental Flow in Shatt Al-Hillah River
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To maintain river flows necessary to meet social and ecological objectives, instream environmental flows are frequently used as a strategy. The capability of three alternative historical flow approaches to protect against low flows is shown in this study using gage stations in the Shatt Al-Hillah River in Iraq. The extension of the Shatt al-Hillah River is the focus of this research discussion on environmental flow assessment. The available data on discharge in this research were adopted for ten years from 2012-2021. Different flow methods were adopted to establish a minimum environmental flow in the Shatt Al-Hillah River. Three hydrological-based approaches: Tennant, modified Tennant, and low-flow metrics like 7Q10, wer

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Publication Date
Sun Jun 07 2009
Journal Name
Baghdad Science Journal
Check List of the algae in Diyala River, Iraq
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A total of 722 algal taxa are recorded in Diyala River by different authors. Most of the identification algae belong to three Divisions: Bacillariophyceae (367, 50.8%), Chlorophyceae (179, 24.8%), and Cyanophyceae (126, 17.5%).

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Publication Date
Sun Jun 30 2024
Journal Name
Modern Sport
The effect of an educational curriculum using virtual reality glasses in improving some kinetic abilities and learning freestyle swimming for first-year primary school students
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هدفت الدراسة الى الاهتمام واستغلال ماهو جديد من تقنيات واجهزة حديثة في تعليم السباحة الحرة عن طريق توجيه الاطفال على تطوير مداركهم واستيعابهم بالتطور التكنولوجي الذي يتناوله العالم ،قامت الباحثتان باعداد منهج تعليمي باستخدام نظارة الواقع الافتراضي وذالك بتوفير بيئة مشابهة للبيئة الحقيقية تحاكي مدارك عقول الاطفال في عالم افتراضي لتتكون صورة كاملة عن مهارات السباحة الحرة ،ومن هنا اتت المشكلة نتيجة تعل

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Publication Date
Sun Jun 07 2009
Journal Name
Baghdad Science Journal
Limits between the Cosmological Parameters from Strong Lensing Observations for Generalized Isothermal Models
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This paper including a gravitational lens time delays study for a general family of lensing potentials, the popular singular isothermal elliptical potential (SIEP), and singular isothermal elliptical density distribution (SIED) but allows general angular structure. At first section there is an introduction for the selected observations from the gravitationally lensed systems. Then section two shows that the time delays for singular isothermal elliptical potential (SIEP) and singular isothermal elliptical density distributions (SIED) have a remarkably simple and elegant form, and that the result for Hubble constant estimations actually holds for a general family of potentials by combining the analytic results with data for the time dela

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Publication Date
Thu Sep 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Some Methods for Estimating Mixture of Linear Regression Models with Application
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 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the

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Publication Date
Thu Sep 30 2021
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of Some Methods for Estimating Mixture of Linear Regression Models with Application
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 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others

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Publication Date
Fri Dec 30 2022
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Artificial Intelligent Models for Detection and Prediction of Lost Circulation Events: A Review
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Lost circulation or losses in drilling fluid is one of the most important problems in the oil and gas industry, and it appeared at the beginning of this industry, which caused many problems during the drilling process, which may lead to closing the well and stopping the drilling process. The drilling muds are relatively expensive, especially the muds that contain oil-based mud or that contain special additives, so it is not economically beneficial to waste and lose these muds. The treatment of drilling fluid losses is also somewhat expensive as a result of the wasted time that it caused, as well as the high cost of materials used in the treatment such as heavy materials, cement, and others. The best way to deal with drilling fluid losses

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Publication Date
Wed Oct 09 2024
Journal Name
Engineering, Technology &amp; Applied Science Research
Improving Pre-trained CNN-LSTM Models for Image Captioning with Hyper-Parameter Optimization
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The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

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Publication Date
Thu Jan 30 2025
Journal Name
Retos
The effects of the integrative strategy on fostering reflective thinking and imparting basic basketball offensive techniques to female students
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Objective: The objective of this study is to develop instructional materials that teach fundamental basketball skills to female students enrolled in the first year of the College of Physical Education and Sports Sciences for Girls at the University of Baghdad while also encouraging reflective thinking (RT). Research methodology: the researcher chose the experimental methodology with the two equal group's method. Finding the community and choosing the right sample for the study's kind and goals are essential to the accomplishment of any research project. A sample for the primary experiment was picked at random after the researcher confirmed the students' first-year basketball curriculum. This study community consisted of (40) fourth-

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