Farshid Rahmani

999 total citations
18 papers, 393 citations indexed

About

Farshid Rahmani is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, Farshid Rahmani has authored 18 papers receiving a total of 393 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Environmental Engineering, 11 papers in Water Science and Technology and 8 papers in Global and Planetary Change. Recurrent topics in Farshid Rahmani's work include Hydrology and Watershed Management Studies (11 papers), Hydrological Forecasting Using AI (9 papers) and Flood Risk Assessment and Management (8 papers). Farshid Rahmani is often cited by papers focused on Hydrology and Watershed Management Studies (11 papers), Hydrological Forecasting Using AI (9 papers) and Flood Risk Assessment and Management (8 papers). Farshid Rahmani collaborates with scholars based in United States, Iran and United Kingdom. Farshid Rahmani's co-authors include Chaopeng Shen, Kathryn Lawson, Alison Appling, Samantha K. Oliver, Jiangtao Liu, Kourosh Behzadian, Li Li, Dapeng Feng, Raj Cibin and Alain N. Rousseau and has published in prestigious journals such as Nature Communications, The Science of The Total Environment and Water Resources Research.

In The Last Decade

Farshid Rahmani

17 papers receiving 387 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Farshid Rahmani United States	11	278	252	157	63	57	18	393
Chen-Min Kuo Taiwan	10	192 0.7×	134 0.5×	235 1.5×	98 1.6×	26 0.5×	14	422
Katayoun Kargar Iran	6	201 0.7×	217 0.9×	215 1.4×	35 0.6×	50 0.9×	8	430
Junxiang Cheng China	9	135 0.5×	215 0.9×	170 1.1×	28 0.4×	13 0.2×	12	324
Yeo Howe Lim United States	7	275 1.0×	298 1.2×	219 1.4×	36 0.6×	63 1.1×	38	482
Amin Haghnegahdar Canada	10	173 0.6×	173 0.7×	161 1.0×	119 1.9×	72 1.3×	20	431
Gengxin Ou United States	11	213 0.8×	229 0.9×	120 0.8×	26 0.4×	49 0.9×	19	386
Baoxu Zhao China	7	232 0.8×	303 1.2×	283 1.8×	55 0.9×	19 0.3×	9	421
T. J. Smith United States	11	211 0.8×	358 1.4×	281 1.8×	40 0.6×	30 0.5×	22	467
Jasna Plavšić Serbia	13	129 0.5×	201 0.8×	287 1.8×	32 0.5×	39 0.7×	35	407

Countries citing papers authored by Farshid Rahmani

Since Specialization

Citations

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

Fields of papers citing papers by Farshid Rahmani

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farshid Rahmani

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

All Works

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18 of 18 papers shown

Song, Yalan, Tadd Bindas, Chaopeng Shen, et al.. (2025). Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning. Nature Communications. 16(1). 9169–9169.

Song, Yalan, Farshid Rahmani, Wei Zhi, et al.. (2024). Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations. Journal of Hydrology. 639. 131573–131573. 9 indexed citations

Rousseau, Alain N., et al.. (2024). Does grouping watersheds by hydrographic regions offer any advantages in fine-tuning transfer learning model for temporal and spatial streamflow predictions?. Journal of Hydrology. 650. 132540–132540. 2 indexed citations

Bindas, Tadd, Wen‐Ping Tsai, Jiangtao Liu, et al.. (2024). Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning. Water Resources Research. 60(1). 33 indexed citations

Rahmani, Farshid, et al.. (2023). A deep learning-based novel approach to generate continuous daily stream nitrate concentration for nitrate data-sparse watersheds. The Science of The Total Environment. 878. 162930–162930. 33 indexed citations

Rousseau, Alain N., et al.. (2023). Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration. Journal of Hydrology. 622. 129682–129682. 34 indexed citations

Rahmani, Farshid, Alison Appling, Dapeng Feng, Kathryn Lawson, & Chaopeng Shen. (2023). Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling. Water Resources Research. 59(12). 15 indexed citations

Song, Yalan, Farshid Rahmani, Wei Zhi, et al.. (2023). Deep Learning Insights into Suspended Sediment Concentrations Across the Conterminous United States: Strengths and Limitations. SSRN Electronic Journal. 2 indexed citations

Liu, Jiangtao, David Hughes, Farshid Rahmani, Kathryn Lawson, & Chaopeng Shen. (2023). Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats. Geoscientific model development. 16(5). 1553–1567. 16 indexed citations

10.

Liu, Jiangtao, Farshid Rahmani, Kathryn Lawson, & Chaopeng Shen. (2022). A Multiscale Deep Learning Model for Soil Moisture Integrating Satellite and In Situ Data. Geophysical Research Letters. 49(7). 59 indexed citations

11.

Rahmani, Farshid, Chaopeng Shen, Samantha K. Oliver, Kathryn Lawson, & Alison Appling. (2021). Deep learning approaches for improving prediction of daily stream temperature in data‐scarce, unmonitored, and dammed basins. Hydrological Processes. 35(11). 51 indexed citations

12.

Rahmani, Farshid, et al.. (2020). Developing and Testing a Long Short-Term Memory Stream Temperature Model in Daily and Continental Scale. 1 indexed citations

13.

Rahmani, Farshid, et al.. (2020). Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data. Environmental Research Letters. 86 indexed citations

14.

Rahmani, Farshid, et al.. (2018). Optimal Operation of Water Distribution Systems Using a Graph Theory–Based Configuration of District Metered Areas. Journal of Water Resources Planning and Management. 144(8). 17 indexed citations

15.

Rahmani, Farshid, Kourosh Behzadian, & Abdollah Ardeshir. (2015). Rehabilitation of a Water Distribution System Using Sequential Multiobjective Optimization Models. Journal of Water Resources Planning and Management. 142(5). 16 indexed citations

16.

Rahmani, Farshid, et al.. (2014). Optimal Rehabilitation Strategy In Water Distribution Systems Considering Reduction In Greenhouse Gas Emissions. UWL Repository (University of West London). 2 indexed citations

17.

Rahmani, Farshid & Kourosh Behzadian. (2014). Sequential Multi-objective Evolutionary Algorithm for a Real-world Water Distribution System Design. Procedia Engineering. 89. 95–102. 6 indexed citations

18.

Rahmani, Farshid, et al.. (2011). Study of wicking behavior of water on woven fabric using magnetic induction technique. Journal of the Textile Institute. 102(7). 559–567. 11 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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