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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
Tue Jan 15 2002
Journal Name
Abhath Al- Yarmouk [basic Sciences And Engineering]
Computer Program for Predicting Ultimate Strength of Structural Concrete Sections of General Shape
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Publication Date
Wed Jan 01 2020
Journal Name
Journal Of Building Engineering
Development of gravitational search algorithm model for predicting packing density of cementitious pastes
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Publication Date
Wed Mar 30 2022
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Crystallization Process as a Final Part of Zero Liquid Discharge System for Treatment of East Baghdad Oilfield Produced Water
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This study investigated the application of the crystallization process for oilfield produced water from the East Baghdad oilfield affiliated to the Midland Oil Company (Iraq). Zero liquid discharge system (ZLD) consists of several parts such as oil skimming, coagulation/flocculation, forward osmosis, and crystallization, the crystallization process is a final part of a zero liquid discharge system. The laboratory-scale simple evaporation system was used to evaluate the performance of the crystallization process. In this work, sodium chloride solution and East Baghdad oilfield produced water were used as a feed solution with a concentration of 177 and 220 g/l. The impact of temperature (70, 80, and 90 °C), mixing speed (300, 400, and 500 rp

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Publication Date
Wed Mar 30 2022
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Crystallization Process as a Final Part of Zero Liquid Discharge System for Treatment of East Baghdad Oilfield Produced Water
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This study investigated the application of the crystallization process for oilfield produced water from the East Baghdad oilfield affiliated to the Midland Oil Company (Iraq). Zero liquid discharge system (ZLD) consists of several parts such as oil skimming, coagulation/flocculation, forward osmosis, and crystallization, the crystallization process is a final part of a zero liquid discharge system. The laboratory-scale simple evaporation system was used to evaluate the performance of the crystallization process. In this work, sodium chloride solution and East Baghdad oilfield produced water were used as a feed solution with a concentration of 177 and 220 g/l. The impact of temperature (70, 80, and 90 °C), mixing speed (300, 400, and 500

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Publication Date
Sun Mar 12 2023
Journal Name
Egyptian Journal Of Chemistry
Studying the Molecular Interactions for Potash Alum with Distilled Water and Aqueous Solution of Ethylene Glycol at Different Temperatures.
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Publication Date
Thu Jun 30 2016
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Modelling and Optimization of Carbon Steel Corrosion in CO2 Containing Oilfield Produced Water in Presence of HAc
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Previously, many empirical models have been used to predict corrosion rates under different CO2 corrosion parameters conditions. Most of these models did not predict the corrosion rate exactly, besides it determined effects of variables by holding some variables constant and changing the values of other variables to obtain the regression model. As a result the experiments will be large and cost too much. In this paper response surface methodology (RSM) was proposed to optimize the experiments and reduce the experimental running. The experiments studied effects of temperature (40 – 60 °C), pH (3-5), acetic acid (HAc) concentration (1000-3000 ppm) and rotation speed (1000-1500 rpm) on CO2 corrosion performance of t

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Publication Date
Thu Sep 01 2016
Journal Name
Journal Of Engineering
Demulsification of Remaining Waste (Water In Oil Emulsions) After Removal Of Phenol In Emulsion Liquid Membrane Process
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The aim of present work is to study the removal of phenol present in aqueous feed solution by the emulsion liquid membrane technique using kerosene as a diluent, sodium hydroxide as a stripping agent, and sorbitan monooleate (Span 80) as a surfactant. The parameters studied were: surfactant concentration, volume ratio of membrane phase to internal phase, and stirring speed. It was found that more than 98% of phenol can be removed at the conditions were surfactant concentration 2% (v/v), volume ratio of membrane phase to internal phase 5:1 and stirring speed 400 rpm. Maximum phenol extraction efficiency at 7 minutes of process time was observed. It was found that there was a good agreement between the standard kerosene an

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Publication Date
Sat Apr 12 2025
Journal Name
Mustansiriyah Journal Of Sports Science
A Review of the Use of Artificial Intelligence Algorithms for Predicting Injuries and Performance in Football Players
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The purpose of this study is to investigate the research on artificial intelligence algorithms in football, specifically in relation to player performance prediction and injury prevention. To accomplish this goal, scholarly resources including Google Scholar, ResearchGate, Springer, and Scopus were used to provide a systematic examination of research done during the last ten years (2015–2025). Through a systematic procedure that included data collection, study selection based on predetermined criteria, categorisation based on AI applications in football, and assessment of major research problems, trends, and prospects, almost fifty papers were found and analysed. Summarising AI applications in football for performance and injury p

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Publication Date
Wed Jun 03 2026
Journal Name
Journal Of Physical Education
Predicting Grip Angle Using Some Kinematical Variables Of Leaving and Flight In Parallel Bar In Men&#39;s Gymnastics (Qatar 2016)
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Publication Date
Thu Feb 29 2024
Journal Name
International Journal Of Design &amp; Nature And Ecodynamics
Artificial Neural Network Assessment of Groundwater Quality for Agricultural Use in Babylon City: An Evaluation of Salinity and Ionic Composition
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