Engineering at university of mosul, Iraq
Prof. Ahmed Younis Mohammed is a Full Professor at the Department of Dams and Water Resources Engineering, College of Engineering, University of Mosul, Iraq. With over two decades of academic and research experience in hydraulic and water resources engineering, he has made significant contributions to the field through teaching, research, and scholarly reviews. He is an active member of international scientific societies like IAHS and IAHR and has served as a peer reviewer for reputed journals published by Elsevier and Taylor’s University.
Professional Profile:
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Education Background
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Master of Science (M.Sc.) in Hydraulics, Department of Water Resources Engineering, University of Mosul, Iraq (2000–2002)
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Bachelor of Science (B.Sc. Eng.) in Irrigation and Drainage Engineering, University of Mosul, Iraq (1992–1996)
Professional Development
Prof. Mohammed has been associated with the University of Mosul since 2003, progressively advancing through academic ranks—from Assistant Lecturer to Full Professor in 2024. He has taught a broad spectrum of undergraduate courses, including Engineering Mechanics, Hydraulics, Fluid Mechanics I & II, Irrigation Principles, MATLAB Programming, and Design of Hydraulic Structures. His academic career has been rooted in the Department of Dams and Water Resources Engineering, where he has consistently contributed to student development and engineering research.
Research Focus
His core research interests include:
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Hydraulics and Hydraulic Structures
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Open Channel Flow and Energy Dissipation
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Hydraulic Modeling and MATLAB Applications
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Design and Analysis of Weirs, Gates, and Dams
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Water Resources Engineering and River Dynamics
His M.Sc. thesis focused on the hydraulic performance of vertical and inclined gates on weirs, contributing valuable insights into flow regulation and structural optimization.
Author Metrics:
Prof. Mohammed has contributed to academic knowledge through multiple publications and scholarly reviews. His expertise is recognized through reviewer certifications from prestigious journals such as:
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Journal of Flow Measurement and Instrumentation (Elsevier, Impact Factor: 1.203)
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Journal of Engineering Science & Technology (JESTEC) (Taylor’s University, SJR: 0.19)
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Scientia Iranica (Elsevier, Impact Factor: 0.679)
Awards and Honors:
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Certificate of Reviewing – Journal of Flow Measurement and Instrumentation, Elsevier
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Certificate of Reviewing – JESTEC, Taylor’s University
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Certificate of Reviewing – Scientia Iranica, Elsevier
Publication Top Notes
📝 1. Machine learning-based modeling of discharge coefficients in labyrinth sluice gates
Journal: Flow Measurement and Instrumentation
Date: March 2025
DOI: 10.1016/j.flowmeasinst.2025.102823
Authors: Thaer Hashem, Ahmed Y. Mohammed, Ali Sharifi
Summary:
This paper presents advanced machine learning models to predict discharge coefficients in labyrinth sluice gates. Various algorithms are evaluated, providing a powerful tool for hydraulic design and optimization. The results show that ML techniques can outperform traditional empirical methods in accuracy and reliability.
📝 2. Flow Characteristics in Vertical Shaft Spillway with Varied Inlet Shapes and Submergence States
Journal: Tikrit Journal of Engineering Sciences
Date: November 24, 2024
DOI: 10.25130/tjes.31.4.4
Authors: Intisar Azher Hadi, Ahmed Younis Mohammed
Summary:
This study investigates the influence of different inlet geometries and submergence levels on the hydraulic behavior of vertical shaft spillways. Using both physical modeling and analytical methods, the authors identify optimal configurations for energy dissipation and flow stability.
📝 3. Unlocking Precision in Hydraulic Engineering: Machine Learning Insights into Labyrinth Sluice Gate Discharge Coefficients
Journal: Journal of Hydroinformatics
Date: November 2024
DOI: 10.2166/hydro.2024.310
Authors: Thaer Hashem, Iman Kattoof Harith, Noor Hassan Alrubaye, Ahmed Y. Mohammed, Mohammed L. Hussien
Summary:
The paper delves into the use of machine learning to enhance accuracy in predicting discharge coefficients for labyrinth sluice gates. It integrates multiple ML models and compares their performance against hydraulic experiment data, pushing the boundaries of smart engineering systems in water structures.
📝 4. Hydraulic Characteristics of Labyrinth Sluice Gate
Journal: Flow Measurement and Instrumentation
Date: April 2024
DOI: 10.1016/j.flowmeasinst.2024.102556
Authors: Thaer Hashem, Ahmed Y. Mohammed, Thair J. Alfatlawi
Summary:
This paper analyzes the hydraulic performance of labyrinth-shaped sluice gates under various flow conditions. The findings offer valuable insights for engineers designing water conveyance systems, focusing on maximizing flow efficiency and minimizing energy loss.
📝 5. Estimating Critical Depth and Discharge over Sloping Rough End Depth Using Machine Learning
Journal: Journal of Hydroinformatics
Date: March 2024
DOI: 10.2166/hydro.2024.242
Authors: Ahmed Y. Mohammed, Parveen Sihag
Summary:
This study employs ML algorithms to estimate critical flow parameters like depth and discharge over rough, sloped surfaces. It demonstrates the capability of ML in modeling complex open-channel hydraulics where traditional approaches may fall short.
Conclusion
Prof. Ahmed Younis Mohammed exemplifies academic excellence, research innovation, and professional service. His pioneering integration of machine learning in hydraulic engineering, extensive publication record, and consistent contributions to engineering education make him highly deserving of the Best Researcher Award in Engineering.
He stands out as a researcher who not only contributes to fundamental knowledge but also applies it to real-world problems in water infrastructure—making him a transformative force in 21st-century civil and environmental engineering.