وب سایت:

 https://scholar.google.com/citations?user=fdjZwh0AAAAJ&hl=en

https://faculty.dlut.edu.cn/AZMAALIASGHAR1/en/index.htm

لینک ارجاعات مقالات:

[1] Azma, A.; Liu, Y.; Eftekhari, M.; Zhang,D. Comparison of hybrid deep learning models for estimation of the time-dependent scour depth downstream of river training structures. Physics of Fluids. 2024, 36, 101911. DOI: 10.1063/5.0231597 (Q1, IF: 4.1 )

[2] Azma, A.; Liu, Y.; Azma, M.; Saadat, M.; Zhang, D.; Cho, J.; Rezania, S.  Hybrid machine learning models for prediction of daily dissolved oxygen. Journal of Water Process Engineering. 2023, 54, 103957. DOI: 10.1016/j.jwpe.2023.103957 (Q1, IF: 6.3)

[3] Azma, A.; Tavakol Sadrabadi, M.; Liu, Y.; Azma, M.; Zhang, D.; Cao, Z.; Li, Z. Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs. Applied Water Science. 2023, 13, 45. DOI: 10.1007/s13201-022-01841-x (Q1, IF: 5.7)

[4] Azma, A.; Behroyan, I.; Babanezhad, M.; Liu, Y. Fuzzy-based bee algorithm for machine learning and pattern recognition of computational data of nanofluid heat transfer. Neural Computing and Applications. 2023, 35, 20087-20101. DOI: 10.1007/s00521-023-08851-z (Q2, IF: 4.5)

[5] Azma, A.; Kiyanfar, R.; Liu, Y.; Azma, M.; Zhang, D.; Cao, Z.; Li, Z. ML and CFD Simulation of flow Structure around Tandem Bridge Piers in Pressurized Flow. Computers, Materials & Continua. 2023, 75, 1711-1733. DOI: 10.32604/cmc.2023.036680 (Q3,  IF: 2.1)

[6] Azma, A.; Narreie, E.; Shojaaddini, A.; Kianfar, N.; Kiyanfar, R.; Seyed Alizadeh, S.; Davarpanah, A. Statistical modeling for spatial groundwater potential map based on GIS technique. Sustainability. 2021, 13, 3788. DOI: 10.3390/su13073788 (Q2, IF: 3.3)

[7] Azma, A.; Zhang, Y. Tributary Channel width effect on the flow behavior in trapezoidal and rectangular channel confluences. Processes. 2020, 8, 1344. DOI: 10.3390/pr8111344 (Q2, IF: 2.8)

[8] Azma, A.; Zhang, Y. The Effect of Variations of Flow from Tributary Channel on the Flow Behavior in a T-Shape Confluence. Processes. 2020, 8, 614. DOI:  10.3390/pr8050614 (Q2, IF: 2.8)

[9] Shirnezhad, Z.; Azma, A.; Foong, LK.; Jahangir, A.; Rastegarnia, A. Assessment of water resources quality of a karstic aquifer in the Southwest of Iran. Bulletin of Engineering Geology and the Environment. 2020, 80, 71-92. DOI: 10.1007/s10064-020-01871-2 (Q1, IF: 3.7)

[10] Honarbakhsh, A.; Azma, A.; Nikseresht, F.; Mousazadeh, M.; Eftekhari, M. Ostovari, Y. Hydro-chemical assessment and GIS-mapping of groundwater quality parameters in semi-arid regions. Journal of Water Supply: Research and Technology-Aqua. 2019, 68, 509-522. DOI: 10.2166/aqua.2019.009 (Q1, IF: 4.3)

[11] Akbari, M.; Tahmoures, M.; Azma, A.; Kiyanfar, R.; Tat shahdost, F. Land capability assessment by combining LESA and GIS in a calcareous watershed, Iran. Arabian Journal of Geosciences. 2022, 15, 404. DOI: 10.1007/s12517-022-09729-5  (Q3, IF: 1.827)

[12] Eslaminezhad, S.; Eftekhari, M.; Azma, A.; Kiyanfar, R.; Akbari, M. Assessment of flood susceptibility prediction based on optimized tree-based machine learning models. Journal of Water and Climate Change. 2022, 13, 2353-2385. DOI: 10.2166/wcc.2022.435 (Q2, IF: 2.7)

[13] Tashayo, B.; Honarbakhsh, A.; Azma, A.; Akbari, M. Combined Fuzzy AHP–GIS for Agricultural Land Suitability Modeling for a Watershed in Southern Iran. Environmental Management. 2020, 66, 364-376. DOI: 10.1007/s00267-020-01310-8 (Q3, IF: 2.7)

[14] Li, Z.; Zhang, D.; Liu, Y.; Azma, A.; Gao, N.  On the unsteady wake flow behind a sphere with large transverse-rotating speeds. Physics Of Fluids. 2023, 35, 105127. DOI: 10.1063/5.0170409 (Q1, IF: 4.1)