N
Nadhir Al-Ansari
Researcher at Luleå University of Technology
Publications - 646
Citations - 10625
Nadhir Al-Ansari is an academic researcher from Luleå University of Technology. The author has contributed to research in topics: Environmental science & Computer science. The author has an hindex of 36, co-authored 513 publications receiving 5887 citations. Previous affiliations of Nadhir Al-Ansari include Al al-Bayt University.
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Experimental and Numerical Analysis for Earth-Fill Dam Seepage
Ahmed Mohammed Sami Al-Janabi,Abdul Halim Ghazali,Yousry Mahmoud Ghazaw,Haitham Abdulmohsin Afan,Nadhir Al-Ansari,Zaher Mundher Yaseen +5 more
TL;DR: In this paper, the authors investigated the seepage through earth-fill dams using physical, mathematical, and numerical models, and the results revealed that both mathematical calculations using L. Casagrande solutions and the SEEP/W numerical model have a plotted SEepage line compatible with the observed SEEPage line in the physical model.
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Management of Water Resources in Iraq: Perspectives and Prognoses
TL;DR: In this article, the government should take measures to have a strategic water management vision, including regional cooperation and coordination, research and development, improving agriculture and sanitation sector as well as public awareness program.
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Waste foundry sand/MgFe-layered double hydroxides composite material for efficient removal of Congo red dye from aqueous solution.
TL;DR: Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g.
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Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Himan Shahabi,Ataollah Shirzadi,Kayvan Ghaderi,Ebrahim Omidvar,Nadhir Al-Ansari,John J. Clague,Marten Geertsema,Khabat Khosravi,Ata Amini,Sepideh Bahrami,Omid Rahmati,Kyoumars Habibi,Ayub Mohammadi,Hoang Nguyen,Assefa M. Melesse,Baharin Bin Ahmad,Anuar Ahmad +16 more
TL;DR: The results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models and should be more widely applied for the sustainable management of flood-prone areas.
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Quang Hung Nguyen,Hai-Bang Ly,Lanh Si Ho,Nadhir Al-Ansari,Hiep Van Le,Van Quan Tran,Indra Prakash,Binh Thai Pham +7 more
TL;DR: The results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.