Pouya Aghelpour

1.0k total citations
27 papers, 791 citations indexed

About

Pouya Aghelpour is a scholar working on Environmental Engineering, Global and Planetary Change and Electrical and Electronic Engineering. According to data from OpenAlex, Pouya Aghelpour has authored 27 papers receiving a total of 791 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Environmental Engineering, 21 papers in Global and Planetary Change and 9 papers in Electrical and Electronic Engineering. Recurrent topics in Pouya Aghelpour's work include Hydrological Forecasting Using AI (21 papers), Hydrology and Drought Analysis (12 papers) and Climate variability and models (9 papers). Pouya Aghelpour is often cited by papers focused on Hydrological Forecasting Using AI (21 papers), Hydrology and Drought Analysis (12 papers) and Climate variability and models (9 papers). Pouya Aghelpour collaborates with scholars based in Iran, Georgia and China. Pouya Aghelpour's co-authors include Babak Mohammadi, Seyed Mostafa Biazar, Özgür Kişi, Renata Graf, Yiqing Guan, Danrong Zhang, Afshin Ashrafzadeh, Ramiro Pillco Zolá, Samad Emamgholizadeh and Saeid Mehdizadeh and has published in prestigious journals such as Scientific Reports, Remote Sensing and Environmental Science and Pollution Research.

In The Last Decade

Pouya Aghelpour

26 papers receiving 778 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pouya Aghelpour Iran 16 478 439 262 172 97 27 791
Seyed Mostafa Biazar Iran 17 424 0.9× 341 0.8× 232 0.9× 158 0.9× 143 1.5× 22 784
Vahid Karimi Iran 13 514 1.1× 266 0.6× 317 1.2× 142 0.8× 102 1.1× 21 808
Keivan Khalili Iran 15 390 0.8× 582 1.3× 265 1.0× 117 0.7× 147 1.5× 36 912
Roozbeh Moazenzadeh Iran 11 339 0.7× 259 0.6× 248 0.9× 95 0.6× 138 1.4× 15 707
Okan Mert Katipoğlu Türkiye 16 379 0.8× 526 1.2× 324 1.2× 105 0.6× 65 0.7× 101 830
Farshad Ahmadi Iran 19 506 1.1× 649 1.5× 388 1.5× 166 1.0× 110 1.1× 52 1.1k
Mahsa Hasanpour Kashani Iran 16 565 1.2× 386 0.9× 371 1.4× 135 0.8× 148 1.5× 30 989
Doudja Souag-Gamane Algeria 13 581 1.2× 557 1.3× 451 1.7× 168 1.0× 157 1.6× 17 1.0k
Yazid Tikhamarine Algeria 13 601 1.3× 452 1.0× 425 1.6× 190 1.1× 194 2.0× 15 1.0k
Ju‐Young Shin South Korea 19 423 0.9× 429 1.0× 234 0.9× 216 1.3× 70 0.7× 42 1.0k

Countries citing papers authored by Pouya Aghelpour

Since Specialization
Citations

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

Fields of papers citing papers by Pouya Aghelpour

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pouya Aghelpour

This figure shows the co-authorship network connecting the top 25 collaborators of Pouya Aghelpour. A scholar is included among the top collaborators of Pouya Aghelpour 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 Pouya Aghelpour. Pouya Aghelpour 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.
Aghelpour, Pouya, et al.. (2024). Numerical Estimation of Surface Soil Moisture by Machine Learning Algorithms in Different Climatic Types. Pure and Applied Geophysics. 181(7). 2149–2175.
2.
Aghelpour, Pouya, et al.. (2023). Coupling ANFIS with ant colony optimization (ACO) algorithm for 1-, 2-, and 3-days ahead forecasting of daily streamflow, a case study in Poland. Environmental Science and Pollution Research. 30(19). 56440–56463. 21 indexed citations
3.
Aghelpour, Pouya, et al.. (2022). Evaluating the predictability of eight Atmospheric-Oceanic signals affecting Iran’s Droughts, employing intelligence based and stochastic methods. Advances in Space Research. 71(5). 2394–2415. 5 indexed citations
4.
Aghelpour, Pouya, et al.. (2022). Comparing three types of data-driven models for monthly evapotranspiration prediction under heterogeneous climatic conditions. Scientific Reports. 12(1). 17363–17363. 13 indexed citations
5.
Aghelpour, Pouya, et al.. (2022). Evaluating the Impact of Large-Scale Climatic Indices as Inputs for Forecasting Monthly River Flow in Mazandaran Province, Iran. Pure and Applied Geophysics. 179(4). 1309–1331. 6 indexed citations
6.
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8.
Raza, Ali, Nadhir Al‐Ansari, Yongguang Hu, et al.. (2022). Misconceptions of Reference and Potential Evapotranspiration: A PRISMA-Guided Comprehensive Review. Hydrology. 9(9). 153–153. 15 indexed citations
9.
Aghelpour, Pouya, et al.. (2021). Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions. Arabian Journal of Geosciences. 14(7). 14 indexed citations
10.
Aghelpour, Pouya, et al.. (2021). Multivariate Drought Forecasting in Short- and Long-Term Horizons Using MSPI and Data-Driven Approaches. Journal of Hydrologic Engineering. 26(4). 22 indexed citations
11.
12.
Graf, Renata & Pouya Aghelpour. (2021). Daily River Water Temperature Prediction: A Comparison between Neural Network and Stochastic Techniques. Atmosphere. 12(9). 1154–1154. 27 indexed citations
13.
Aghelpour, Pouya, et al.. (2021). Hydrological drought forecasting using multi-scalar streamflow drought index, stochastic models and machine learning approaches, in northern Iran. Stochastic Environmental Research and Risk Assessment. 35(8). 1615–1635. 51 indexed citations
14.
Aghelpour, Pouya, et al.. (2021). A novel hybrid dragonfly optimization algorithm for agricultural drought prediction. Stochastic Environmental Research and Risk Assessment. 35(12). 2459–2477. 49 indexed citations
15.
Aghelpour, Pouya, et al.. (2020). A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods. ISPRS International Journal of Geo-Information. 9(12). 701–701. 38 indexed citations
16.
Mohammadi, Babak, Yiqing Guan, Pouya Aghelpour, et al.. (2020). Simulation of Titicaca Lake Water Level Fluctuations Using Hybrid Machine Learning Technique Integrated with Grey Wolf Optimizer Algorithm. Water. 12(11). 3015–3015. 67 indexed citations
17.
Aghelpour, Pouya, et al.. (2020). Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series. Stochastic Environmental Research and Risk Assessment. 34(1). 33–50. 57 indexed citations
18.
Aghelpour, Pouya, et al.. (2020). Using the MODIS Sensor for Snow Cover Modeling and the Assessment of Drought Effects on Snow Cover in a Mountainous Area. Remote Sensing. 12(20). 3437–3437. 29 indexed citations
19.
Ashrafzadeh, Afshin, et al.. (2020). Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran. Journal of Irrigation and Drainage Engineering. 146(6). 59 indexed citations
20.
Aghelpour, Pouya, Babak Mohammadi, & Seyed Mostafa Biazar. (2019). Long-term monthly average temperature forecasting in some climate types of Iran, using the models SARIMA, SVR, and SVR-FA. Theoretical and Applied Climatology. 138(3-4). 1471–1480. 109 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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