Ahmed Mohammed Sami Al-Janabi -

Dr.

Ahmed Mohammed Sami Al-Janabi

Research Interests

Gender	MALE
Place of Work	Presidency
Position	Officer of the Research Centers Oversight Unit
Qualification	PhD (Doctorate)
Speciality	Civil Engineering
Email	ahmed.aljanabi@ntu.edu.iq
Phone	07714420395
Address	Mosul, Mosul, Mosul, Iraq

Academic Links

Dr. Ahmed Mohammed Sami Al-Janabi

Department of Scientific Affairs, Northern Technical University, Iraq.
Lecturer, Department of Building and Construction, Engineering Technical College of Mosul, Northern Technical University, Mosul, 41002, Iraq.
Dr. Ahmed Al-Janabi is a distinguished academic and researcher currently working in the Department of Scientific Affairs, at the Presidency of Northern Technical University, Mosul, 41002, Iraq. Moreover, he is a lecturer in the Department of Building and Construction, Engineering Technical College of Mosul, Northern Technical University (NTU).
Dr. Al-Janabi earned his Ph.D. in Civil Engineering specialized in Water Resources Engineering from University Putra Malaysia (UPM) in 2018. He also holds an MSc in Water Engineering (Civil) from University Putra Malaysia (UPM) (2013), and a B.Sc. in Building and Construction (Civil Eng.) from the University of Technology/ Baghdad, (2002).
Dr. Al-Janabi has published over of 30 peer-reviewed articles, Most of which are indexed in esteemed academic databases, including Scopus and Web of Science. His scholarly impact is reflected in his citation metrics, with over 430 citations on Google Scholar, an h-index of 11, 314 citations on Scopus with an h-index of 10, and 242 citations on Web of Science (WOS) with an h-index of 10. In addition to his numerous research articles, Dr. Al-Janabi is the author of a book.
Dr. Al-Janabi's research primarily focuses on Water Resources Engineering, Construction Project Management, Hydraulics and Hydrology, Reservoirs and Dams, Stormwater Management. He received a research award from Sivapalan Young Scientists Travel Awards (SYSTA) that sponsored by: International Association of Hydrological Sciences (IAHS).
Beyond his research and teaching roles, Dr. Al-Janabi serves as a respected reviewer for several prestigious academic journals, indexed in web of science (WOS).

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Research award from Sivapalan Young Scientists Travel Awards (SYSTA) that sponsored by: International Association of Hydrological Sciences (IAHS).

Languages

Arabic (100%)

English (85%)

Skills

Speaker, Lecturer and Trainer (95%)

Academic Researcher (90%)

Managerial Skills (90%)

Communication Skills (95%)

Lecturer (80%)

working experience

Academic Qualification

PhD. Water Resources Engineering (Civil Engineering)

Sep 12, 2013 - Jul 12, 2018

University Putra Malaysia (UPM)

Master of Water Engineering

Sep 12, 2011 - Jul 12, 2013

University Putra Malaysia (UPM)

Building and Construction (Civil Engineering)

Sep 12, 1998 - Jun 12, 2002

University of Technology/ Baghdad

Working Experience

Academic Researcher [Lecturer]

Jul 1, 2018 - Present

Northern Technical University (NTU) [Lecturer]

Nov 11, 2024 - Present

Scientific Affairs Department

Publications

Harnessing Deep Learning and Snow Cover Data for Enhanced Runoff Prediction in Snow-Dominated Watersheds

Nov 22, 2024

Journal Atmosphere

publisher MDPI

DOI https://doi.org/10.3390/atmos15121407

Issue 12

Volume 15

Predicting streamflow is essential for managing water resources, especially in basins and watersheds where snowmelt plays a major role in river discharge. This study evaluates the advanced deep learning models for accurate monthly and peak streamflow forecasting in the Gilgit River Basin. The models utilized were LSTM, BiLSTM, GRU, CNN, and their hybrid combinations (CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU). Our research measured the model’s accuracy through root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and the coefficient of determination (R2). The findings indicated that the hybrid models, especially CNN-BiGRU and CNN-BiLSTM, achieved much better performance than traditional models like LSTM and GRU. For instance, CNN-BiGRU achieved the lowest RMSE (71.6 in training and 95.7 in testing) and the highest R2 (0.962 in training and 0.929 in testing). A novel aspect of this research was the integration of MODIS-derived snow-covered area (SCA) data, which enhanced model accuracy substantially. When SCA data were included, the CNN-BiLSTM model’s RMSE improved from 83.6 to 71.6 during training and from 108.6 to 95.7 during testing. In peak streamflow prediction, CNN-BiGRU outperformed other models with the lowest absolute error (108.4), followed by CNN-BiLSTM (144.1). This study’s results reinforce the notion that combining CNN’s spatial feature extraction capabilities with the temporal dependencies captured by LSTM or GRU significantly enhances model accuracy. The demonstrated improvements in prediction accuracy, especially for extreme events, highlight the potential for these models to support more informed decision-making in flood risk management and water allocation.