Mohammad Azarafza

2.8k total citations · 4 hit papers
50 papers, 2.0k citations indexed

About

Mohammad Azarafza is a scholar working on Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality and Civil and Structural Engineering. According to data from OpenAlex, Mohammad Azarafza has authored 50 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Management, Monitoring, Policy and Law, 25 papers in Safety, Risk, Reliability and Quality and 22 papers in Civil and Structural Engineering. Recurrent topics in Mohammad Azarafza's work include Landslides and related hazards (32 papers), Geotechnical Engineering and Analysis (25 papers) and Rock Mechanics and Modeling (19 papers). Mohammad Azarafza is often cited by papers focused on Landslides and related hazards (32 papers), Geotechnical Engineering and Analysis (25 papers) and Rock Mechanics and Modeling (19 papers). Mohammad Azarafza collaborates with scholars based in Iran, Türkiye and Netherlands. Mohammad Azarafza's co-authors include Haluk Akgün, Reza Derakhshani, Ebrahim Asghari-Kaljahi, Yaser A. Nanehkaran, Akbar Ghazifard, Mehdi Azarafza, Peter M. Atkinson, Junde Chen, Ahmed Cemiloglu and Li-Cai Zhu and has published in prestigious journals such as Scientific Reports, Materials and Engineering Geology.

In The Last Decade

Mohammad Azarafza

50 papers receiving 2.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep learning-based landslide susceptibility mapping

2021 288 citations Mohammad Azarafza, Mehdi Azarafza et al. Scientific Reports profile →
Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

2023 117 citations Yaser A. Nanehkaran, Biyun Chen et al. Water profile →
Comparative Analysis for Slope Stability by Using Machine Learning Methods

2023 76 citations Yaser A. Nanehkaran, Li-Cai Zhu et al. Applied Sciences profile →
Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin

2024 55 citations Yimin Mao, Fei Teng et al. Water profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mohammad Azarafza Iran	27	1.3k	722	675	479	423	50	2.0k
Haluk Akgün Türkiye	25	982 0.8×	660 0.9×	810 1.2×	476 1.0×	319 0.8×	87	1.9k
Suzanne Lacasse Norway	18	1.4k 1.1×	840 1.2×	1.4k 2.1×	268 0.6×	537 1.3×	65	2.6k
Junwei Ma China	24	994 0.8×	323 0.4×	707 1.0×	349 0.7×	185 0.4×	52	1.6k
Reza Derakhshani Iran	25	863 0.7×	445 0.6×	405 0.6×	259 0.5×	327 0.8×	102	2.1k
Qiqing Wang China	27	1.0k 0.8×	580 0.8×	273 0.4×	644 1.3×	694 1.6×	73	1.9k
Danqing Song China	24	988 0.8×	576 0.8×	897 1.3×	597 1.2×	151 0.4×	100	1.7k
Xing-guo Yang China	28	1.1k 0.9×	505 0.7×	1.1k 1.7×	612 1.3×	242 0.6×	131	2.1k
Maria Ferentinou South Africa	20	802 0.6×	442 0.6×	506 0.7×	189 0.4×	320 0.8×	39	1.3k
Dongpo Wang China	25	1.3k 1.0×	367 0.5×	697 1.0×	463 1.0×	269 0.6×	78	1.8k

Countries citing papers authored by Mohammad Azarafza

Since Specialization

Citations

This map shows the geographic impact of Mohammad Azarafza's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mohammad Azarafza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Azarafza more than expected).

Fields of papers citing papers by Mohammad Azarafza

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mohammad Azarafza. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mohammad Azarafza. The network helps show where Mohammad Azarafza may publish in the future.

Co-authorship network of co-authors of Mohammad Azarafza

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Azarafza. A scholar is included among the top collaborators of Mohammad Azarafza based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Mohammad Azarafza. Mohammad Azarafza is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Mao, Yimin, et al.. (2025). Innovative stability analysis of complex secondary toppling failures in rock slopes using the block theory. Landslides. 22(6). 2037–2054. 13 indexed citations

Ghassemi, Saba, et al.. (2025). Innovative insights into micropile seismic response: Shaking table tests reveal critical dependencies and liquefaction mitigation. Bulletin of Engineering Geology and the Environment. 84(4). 1 indexed citations

Mao, Yimin, et al.. (2024). Predicting the elasticity modulus of sedimentary rocks using Deep Random Forest Optimization (DRFO) algorithm. Environmental Earth Sciences. 83(16). 14 indexed citations

Mao, Yimin, et al.. (2024). Utilizing Hybrid Machine Learning and Soft Computing Techniques for Landslide Susceptibility Mapping in a Drainage Basin. Water. 16(3). 380–380. 55 indexed citations breakdown →

Cemiloglu, Ahmed, et al.. (2023). Support Vector Machine (SVM) Application for Uniaxial Compression Strength (UCS) Prediction: A Case Study for Maragheh Limestone. Applied Sciences. 13(4). 2217–2217. 43 indexed citations

Nanehkaran, Yaser A., Biyun Chen, Ahmed Cemiloglu, et al.. (2023). Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals. Water. 15(15). 2707–2707. 117 indexed citations breakdown →

Nanehkaran, Yaser A., et al.. (2023). Comparative Analysis for Slope Stability by Using Machine Learning Methods. Applied Sciences. 13(3). 1555–1555. 76 indexed citations breakdown →

Cemiloglu, Ahmed, et al.. (2023). Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm. Land. 12(7). 1397–1397. 67 indexed citations

Azarafza, Mohammad, et al.. (2022). A Qslope-based empirical method to stability assessment of mountain rock slopes in multiple faults zone: A case for North of Tabriz. MethodsX. 9. 101718–101718. 5 indexed citations

10.

Nanehkaran, Yaser A., Li-Cai Zhu, Junde Chen, Mohammad Azarafza, & Yimin Mao. (2022). Application of artificial neural networks and geographic information system to provide hazard susceptibility maps for rockfall failures. Environmental Earth Sciences. 81(19). 45 indexed citations

11.

Nanehkaran, Yaser A., et al.. (2021). Fuzzy-based multiple decision method for landslide susceptibility and hazard assessment: A case study of Tabriz, Iran. Geomechanics and Engineering. 24(5). 407–418. 52 indexed citations

12.

Azarafza, Mohammad, Yaser A. Nanehkaran, Haluk Akgün, & Yimin Mao. (2021). Application of an image processing-based algorithm for river-side granular sediment gradation distribution analysis. OpenMETU (Middle East Technical University). 10(3). 229–244. 18 indexed citations

13.

Azarafza, Mohammad, Mehdi Azarafza, Haluk Akgün, Peter M. Atkinson, & Reza Derakhshani. (2021). Deep learning-based landslide susceptibility mapping. Scientific Reports. 11(1). 24112–24112. 288 indexed citations breakdown →

14.

Azarafza, Mohammad, et al.. (2021). An empirical classification method for South Pars marls by Schmidt hammer rebound index. MethodsX. 8. 101366–101366. 7 indexed citations

15.

Azarafza, Mohammad, et al.. (2021). Correlations of SMR-Qslope Data in Stability Classification of Discontinuous Rock Slope: A Modified Relationship Considering the Iranian Data. Geotechnical and Geological Engineering. 40(4). 1751–1764. 20 indexed citations

16.

Azarafza, Mohammad, et al.. (2020). The impact of municipal waste disposal of heavy metals on environmental pollution: A case study for Tonekabon, Iran. Advances in Environmental Research. 9(3). 175–189. 8 indexed citations

17.

Azarafza, Mohammad, Haluk Akgün, Mohammad‐Reza Feizi‐Derakhshi, et al.. (2020). Discontinuous rock slope stability analysis under blocky structural sliding by fuzzy key-block analysis method. Heliyon. 6(5). e03907–e03907. 37 indexed citations

18.

Azarafza, Mohammad, Akbar Ghazifard, Haluk Akgün, & Ebrahim Asghari-Kaljahi. (2019). Geotechnical characteristics and empirical geo-engineering relations of the South Pars Zone marls, Iran. Geomechanics and Engineering. 19(5). 393–405. 13 indexed citations

19.

Azarafza, Mohammad, Haluk Akgün, & Ebrahim Asghari-Kaljahi. (2017). Assessment of rock slope stability by slope mass rating (SMR): A case study for the gas flare site in Assalouyeh, South of Iran. Geomechanics and Engineering. 13(4). 571–584. 19 indexed citations

20.

Azarafza, Mohammad, et al.. (2015). Dynamic stability analysis of jointed rock slopes under earthquake condition (Case study: Gas Flare Site of phase 7 in South Pars gas complex- Assalouyeh). 8. 67–78. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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