Morteza Pakdaman

890 total citations
40 papers, 663 citations indexed

About

Morteza Pakdaman is a scholar working on Modeling and Simulation, Control and Systems Engineering and Atmospheric Science. According to data from OpenAlex, Morteza Pakdaman has authored 40 papers receiving a total of 663 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Modeling and Simulation, 9 papers in Control and Systems Engineering and 9 papers in Atmospheric Science. Recurrent topics in Morteza Pakdaman's work include Fractional Differential Equations Solutions (10 papers), Climate variability and models (9 papers) and Meteorological Phenomena and Simulations (8 papers). Morteza Pakdaman is often cited by papers focused on Fractional Differential Equations Solutions (10 papers), Climate variability and models (9 papers) and Meteorological Phenomena and Simulations (8 papers). Morteza Pakdaman collaborates with scholars based in Iran, Malaysia and Türkiye. Morteza Pakdaman's co-authors include Sohrab Effati, Soheil Salahshour, Ali Ahmadian, Alireza Pooya, Hadi Sadoghi Yazdi, Dumitru Bǎleanu, Iman Babaeian, Lotfi Tadj, Массимилиано Феррара and Norazak Senu and has published in prestigious journals such as Water Resources Research, Journal of Environmental Management and Information Sciences.

In The Last Decade

Morteza Pakdaman

39 papers receiving 614 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Morteza Pakdaman Iran 14 288 161 158 130 105 40 663
Jianke Zhang China 15 84 0.3× 138 0.9× 50 0.3× 174 1.3× 77 0.7× 65 812
Svetoslav Markov Bulgaria 14 105 0.4× 105 0.7× 64 0.4× 120 0.9× 71 0.7× 80 829
Xavier Warin France 11 57 0.2× 47 0.3× 163 1.0× 55 0.4× 76 0.7× 37 781
Clémentine Prieur France 16 30 0.1× 55 0.3× 89 0.6× 159 1.2× 37 0.4× 61 1.1k
Francesco Russo France 20 111 0.4× 156 1.0× 68 0.4× 11 0.1× 58 0.6× 109 1.4k
F. M. Maalek Ghaini Iran 18 785 2.7× 140 0.9× 236 1.5× 58 0.4× 495 4.7× 39 1.1k
Dante Kalise United Kingdom 13 63 0.2× 159 1.0× 96 0.6× 30 0.2× 50 0.5× 40 526
Nikolay Kyurkchiev Bulgaria 11 79 0.3× 19 0.1× 66 0.4× 99 0.8× 152 1.4× 113 634
Ilya Pavlyukevich Germany 11 48 0.2× 58 0.4× 261 1.7× 246 1.9× 14 0.1× 36 808
Serge B. Provost Canada 15 32 0.1× 66 0.4× 71 0.4× 196 1.5× 16 0.2× 81 1.1k

Countries citing papers authored by Morteza Pakdaman

Since Specialization
Citations

This map shows the geographic impact of Morteza Pakdaman'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 Morteza Pakdaman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Morteza Pakdaman more than expected).

Fields of papers citing papers by Morteza Pakdaman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Morteza Pakdaman. 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 Morteza Pakdaman. The network helps show where Morteza Pakdaman may publish in the future.

Co-authorship network of co-authors of Morteza Pakdaman

This figure shows the co-authorship network connecting the top 25 collaborators of Morteza Pakdaman. A scholar is included among the top collaborators of Morteza Pakdaman 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 Morteza Pakdaman. Morteza Pakdaman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Pakdaman, Morteza, et al.. (2024). A review of the applications of computational decision intelligence approaches in agrometeorology. Elsevier eBooks. 133–142. 1 indexed citations
2.
Pakdaman, Morteza, et al.. (2024). Bayesian Joint Probability Approach for Post‐Processing Monthly Precipitation Prediction in Northwest Iran. Water Resources Research. 60(8). 2 indexed citations
3.
Pooya, Alireza, et al.. (2024). A new bi-objective simultaneous model for timetabling and scheduling public bus transportation. OPSEARCH. 62(1). 198–229. 2 indexed citations
4.
Pakdaman, Morteza, et al.. (2024). A hybrid approach for generating daily 2m temperature of 1km spatial resolution over Iran. Theoretical and Applied Climatology. 155(8). 7109–7119. 1 indexed citations
5.
Pakdaman, Morteza, et al.. (2024). Artificial intelligence and decision making in climate change studies: A review. Elsevier eBooks. 109–118. 1 indexed citations
6.
Pakdaman, Morteza, et al.. (2023). Statistical postprocessing of dynamically downscaled outputs of CFS.v2. Stochastic Environmental Research and Risk Assessment. 37(6). 2379–2397. 4 indexed citations
7.
Zenner, Eric K., et al.. (2023). Spatially explicit modeling of disease surveillance in mixed oak-hardwood forests based on machine-learning algorithms. Journal of Environmental Management. 337. 117714–117714. 5 indexed citations
8.
Ahmadian, Ali, et al.. (2023). A highly accurate artificial neural networks scheme for solving higher multi‐order fractal‐fractional differential equations based on generalized Caputo derivative. International Journal for Numerical Methods in Engineering. 124(19). 4371–4404. 4 indexed citations
9.
Pakdaman, Morteza, Iman Babaeian, & Laurens M. Bouwer. (2022). Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms. Water. 14(17). 2632–2632. 8 indexed citations
10.
Senu, Norazak, et al.. (2022). A new iterative technique for solving fractal-fractional differential equations based on artificial neural network in the new generalized Caputo sense. Engineering With Computers. 39(1). 505–515. 13 indexed citations
11.
Pakdaman, Morteza, et al.. (2022). European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation. Water Resources Management. 36(2). 611–623. 17 indexed citations
12.
Pakdaman, Morteza, et al.. (2021). Revisiting albedo from a fuzzy perspective. Kybernetes. 51(10). 2927–2937. 1 indexed citations
13.
Pakdaman, Morteza, et al.. (2021). Homogenization and trend detection of temperature in Iran for the period 1960–2018. Meteorology and Atmospheric Physics. 133(4). 1233–1250. 14 indexed citations
14.
Pakdaman, Morteza, et al.. (2020). A kernel least mean square algorithm for fuzzy differential equations and its application in earth’s energy balance model and climate. Alexandria Engineering Journal. 59(4). 2803–2810. 16 indexed citations
15.
Pakdaman, Morteza, et al.. (2020). Lightning prediction using an ensemble learning approach for northeast of Iran. Journal of Atmospheric and Solar-Terrestrial Physics. 209. 105417–105417. 14 indexed citations
16.
Pooya, Alireza & Morteza Pakdaman. (2017). Optimal control model for finite capacity continuous MRP with deteriorating items. Journal of Intelligent Manufacturing. 30(5). 2203–2215. 13 indexed citations
17.
Pakdaman, Morteza & Sohrab Effati. (2015). Approximating the Solution of Optimal Control Problems by Fuzzy Systems. Neural Processing Letters. 43(3). 667–686. 16 indexed citations
18.
Effati, Sohrab & Morteza Pakdaman. (2012). Optimal control problem via neural networks. Neural Computing and Applications. 23(7-8). 2093–2100. 49 indexed citations
19.
Effati, Sohrab & Morteza Pakdaman. (2010). Artificial neural network approach for solving fuzzy differential equations. Information Sciences. 180(8). 1434–1457. 108 indexed citations
20.
Yazdi, Hadi Sadoghi, Morteza Pakdaman, & Sohrab Effati. (2008). Fuzzy Circuit Analysis. International Journal of Applied Engineering Research. 3. 7 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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