Saeid Mehdizadeh

2.1k total citations
42 papers, 1.7k citations indexed

About

Saeid Mehdizadeh is a scholar working on Environmental Engineering, Global and Planetary Change and Water Science and Technology. According to data from OpenAlex, Saeid Mehdizadeh has authored 42 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Environmental Engineering, 22 papers in Global and Planetary Change and 15 papers in Water Science and Technology. Recurrent topics in Saeid Mehdizadeh's work include Hydrological Forecasting Using AI (27 papers), Plant Water Relations and Carbon Dynamics (13 papers) and Hydrology and Watershed Management Studies (12 papers). Saeid Mehdizadeh is often cited by papers focused on Hydrological Forecasting Using AI (27 papers), Plant Water Relations and Carbon Dynamics (13 papers) and Hydrology and Watershed Management Studies (12 papers). Saeid Mehdizadeh collaborates with scholars based in Iran, Türkiye and China. Saeid Mehdizadeh's co-authors include Babak Mohammadi, Javad Behmanesh, Keivan Khalili, Farshad Ahmadi, Farshad Fathian, Mir Jafar Sadegh Safari, Quoc Bao Pham, Jan Adamowski, Nguyễn Thị Thùy Linh and Zheng Duan and has published in prestigious journals such as Journal of Hydrology, Applied Thermal Engineering and Separation and Purification Technology.

In The Last Decade

Saeid Mehdizadeh

39 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Saeid Mehdizadeh Iran 27 1.0k 892 547 374 327 42 1.7k
Hadi Sanikhani Iran 23 925 0.9× 630 0.7× 496 0.9× 276 0.7× 237 0.7× 41 1.5k
Saeed Samadianfard Iran 24 951 0.9× 630 0.7× 670 1.2× 290 0.8× 230 0.7× 54 1.9k
Mohammad Taghi Sattari Iran 21 858 0.8× 689 0.8× 650 1.2× 228 0.6× 206 0.6× 68 1.6k
Sujay Raghavendra Naganna India 20 864 0.8× 470 0.5× 583 1.1× 168 0.4× 169 0.5× 57 1.8k
Mohammad Ali Ghorbani Iran 18 640 0.6× 511 0.6× 406 0.7× 188 0.5× 174 0.5× 34 1.4k
Rana Muhammad Adnan China 30 1.6k 1.5× 1.1k 1.3× 1.3k 2.3× 450 1.2× 491 1.5× 71 2.7k
P. C. Nayak India 15 1.2k 1.1× 1.0k 1.1× 919 1.7× 203 0.5× 277 0.8× 22 1.8k
Hatice Çıtakoğlu Türkiye 19 505 0.5× 639 0.7× 312 0.6× 296 0.8× 253 0.8× 43 1.3k
Hanmi Zhou China 12 478 0.5× 478 0.5× 214 0.4× 440 1.2× 282 0.9× 29 1.5k
Pau Martí Spain 24 615 0.6× 975 1.1× 528 1.0× 380 1.0× 112 0.3× 41 1.5k

Countries citing papers authored by Saeid Mehdizadeh

Since Specialization
Citations

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

Fields of papers citing papers by Saeid Mehdizadeh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Saeid Mehdizadeh

This figure shows the co-authorship network connecting the top 25 collaborators of Saeid Mehdizadeh. A scholar is included among the top collaborators of Saeid Mehdizadeh 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 Saeid Mehdizadeh. Saeid Mehdizadeh 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
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Band, Shahab S., et al.. (2024). Deep learning hybrid models with multivariate variational mode decomposition for estimating daily solar radiation. Alexandria Engineering Journal. 105. 613–625. 2 indexed citations
3.
Li, Shuguang, et al.. (2024). Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River. Engineering Applications of Computational Fluid Mechanics. 18(1). 8 indexed citations
4.
Band, Shahab S., Rasoul Ameri, Sultan Noman Qasem, et al.. (2024). A two-stage deep learning-based hybrid model for daily wind speed forecasting. Heliyon. 11(1). e41026–e41026. 2 indexed citations
5.
Danesh, Malihe, et al.. (2024). A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting. Water Resources Management. 39(4). 1911–1930. 15 indexed citations
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Ahmadi, Farshad, Saeid Mehdizadeh, & Vahid Nourani. (2022). Improving the performance of random forest for estimating monthly reservoir inflow via complete ensemble empirical mode decomposition and wavelet analysis. Stochastic Environmental Research and Risk Assessment. 36(9). 2753–2768. 26 indexed citations
8.
Mehdizadeh, Saeid, Babak Mohammadi, & Farshad Ahmadi. (2022). Establishing Coupled Models for Estimating Daily Dew Point Temperature Using Nature-Inspired Optimization Algorithms. Hydrology. 9(1). 9–9. 15 indexed citations
9.
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
10.
Mehdizadeh, Saeid, Babak Mohammadi, Quoc Bao Pham, & Zheng Duan. (2021). Development of Boosted Machine Learning Models for Estimating Daily Reference Evapotranspiration and Comparison with Empirical Approaches. Water. 13(24). 3489–3489. 38 indexed citations
11.
Mehdizadeh, Saeid, Farshad Ahmadi, Ali Danandeh Mehr, & Mir Jafar Sadegh Safari. (2020). Drought modeling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology. 587. 125017–125017. 60 indexed citations
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Mohammadi, Babak & Saeid Mehdizadeh. (2020). Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm. Agricultural Water Management. 237. 106145–106145. 209 indexed citations
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Fathian, Farshad, et al.. (2019). Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models. Journal of Hydrology. 575. 1200–1213. 104 indexed citations
16.
Mehdizadeh, Saeid, et al.. (2018). A Comparative Study of Autoregressive, Autoregressive Moving Average, Gene Expression Programming and Bayesian Networks for Estimating Monthly Streamflow. Water Resources Management. 32(9). 3001–3022. 39 indexed citations
17.
Mehdizadeh, Saeid, Javad Behmanesh, & Keivan Khalili. (2017). Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration. Computers and Electronics in Agriculture. 139. 103–114. 184 indexed citations
18.
Mehdizadeh, Saeid. (2017). Assessing the potential of data-driven models for estimation of long-term monthly temperatures. Computers and Electronics in Agriculture. 144. 114–125. 37 indexed citations
19.
Mehdizadeh, Saeid, Javad Behmanesh, & Keivan Khalili. (2017). A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model. Journal of Hydrology. 554. 721–742. 37 indexed citations
20.
Mehdizadeh, Saeid, et al.. (2016). Calibration of Hargreaves–Samani and Priestley–Taylor equations in estimating reference evapotranspiration in the Northwest of Iran. Archives of Agronomy and Soil Science. 63(7). 942–955. 18 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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