Dapeng Feng

2.0k total citations · 2 hit papers
13 papers, 883 citations indexed

About

Dapeng Feng is a scholar working on Water Science and Technology, Environmental Engineering and Global and Planetary Change. According to data from OpenAlex, Dapeng Feng has authored 13 papers receiving a total of 883 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Water Science and Technology, 10 papers in Environmental Engineering and 10 papers in Global and Planetary Change. Recurrent topics in Dapeng Feng's work include Hydrology and Watershed Management Studies (13 papers), Hydrological Forecasting Using AI (10 papers) and Flood Risk Assessment and Management (7 papers). Dapeng Feng is often cited by papers focused on Hydrology and Watershed Management Studies (13 papers), Hydrological Forecasting Using AI (10 papers) and Flood Risk Assessment and Management (7 papers). Dapeng Feng collaborates with scholars based in United States, China and Saudi Arabia. Dapeng Feng's co-authors include Chaopeng Shen, Kathryn Lawson, Wen‐Ping Tsai, Jiangtao Liu, Wei Zhi, Gary Sterle, A. A. Harpold, Li Li, Kuai Fang and Daniel Kifer and has published in prestigious journals such as Environmental Science & Technology, Water Resources Research and Geophysical Research Letters.

In The Last Decade

Dapeng Feng

13 papers receiving 870 citations

Hit Papers

From Hydrometeorology to River Water Quality: Can a Deep ... 2021 2026 2022 2024 2021 2022 50 100 150 200
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.

  1. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?
    2021 204 citations Wei Zhi, Dapeng Feng et al. Environmental Science & Technology profile →
  2. Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy
    2022 142 citations Dapeng Feng, Jiangtao Liu et al. Water Resources Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dapeng Feng United States 12 707 632 456 112 44 13 883
Alden Keefe Sampson United States 7 814 1.2× 766 1.2× 658 1.4× 152 1.4× 24 0.5× 10 1.0k
Cristina Prieto Spain 10 656 0.9× 494 0.8× 550 1.2× 108 1.0× 19 0.4× 23 819
Martin Gauch United States 13 771 1.1× 718 1.1× 650 1.4× 187 1.7× 21 0.5× 25 1.0k
Kuai Fang United States 8 406 0.6× 464 0.7× 307 0.7× 134 1.2× 19 0.4× 17 653
Patricia Jimeno‐Sáez Spain 16 532 0.8× 319 0.5× 437 1.0× 92 0.8× 19 0.4× 27 698
Mohamad Javad Alizadeh Iran 14 475 0.7× 488 0.8× 201 0.4× 79 0.7× 36 0.8× 18 819
Murat Ay Türkiye 9 365 0.5× 285 0.5× 358 0.8× 109 1.0× 29 0.7× 21 701
Jiangtao Liu United States 12 289 0.4× 267 0.4× 236 0.5× 118 1.1× 32 0.7× 22 513
Shervan Gharari Canada 19 980 1.4× 472 0.7× 780 1.7× 237 2.1× 22 0.5× 35 1.2k
Sabahattin Işık United States 8 364 0.5× 261 0.4× 239 0.5× 51 0.5× 18 0.4× 15 506

Countries citing papers authored by Dapeng Feng

Since Specialization
Citations

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

Fields of papers citing papers by Dapeng Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dapeng Feng

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

All Works

13 of 13 papers shown
1.
Yang, Yuan, Dapeng Feng, Hylke E. Beck, et al.. (2025). Global Daily Discharge Estimation Based on Grid Long Short‐Term Memory (LSTM) Model and River Routing. Water Resources Research. 61(6). 2 indexed citations
2.
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
3.
Song, Yalan, Wouter Knoben, Martyn Clark, et al.. (2024). When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling. Hydrology and earth system sciences. 28(13). 3051–3077. 12 indexed citations
4.
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
5.
Yao, Yingying, Yufeng Zhao, Xin Li, et al.. (2023). Can transfer learning improve hydrological predictions in the alpine regions?. Journal of Hydrology. 625. 130038–130038. 25 indexed citations
6.
Feng, Dapeng, Hylke E. Beck, Kathryn Lawson, & Chaopeng Shen. (2023). The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment. Hydrology and earth system sciences. 27(12). 2357–2373. 64 indexed citations
7.
Fang, Kuai, Daniel Kifer, Kathryn Lawson, Dapeng Feng, & Chaopeng Shen. (2022). The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology. Water Resources Research. 58(4). 86 indexed citations
8.
Feng, Dapeng, Jiangtao Liu, Kathryn Lawson, & Chaopeng Shen. (2022). Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy. Water Resources Research. 58(10). 142 indexed citations breakdown →
9.
Ma, Kai, Dapeng Feng, Kathryn Lawson, et al.. (2021). Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions. Water Resources Research. 57(5). 121 indexed citations
10.
Lawson, Kathryn, et al.. (2021). Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy. Journal of Hydrology. 599. 126455–126455. 66 indexed citations
11.
Feng, Dapeng, Kathryn Lawson, & Chaopeng Shen. (2021). Mitigating Prediction Error of Deep Learning Streamflow Models in Large Data‐Sparse Regions With Ensemble Modeling and Soft Data. Geophysical Research Letters. 48(14). 71 indexed citations
12.
Zhi, Wei, Dapeng Feng, Wen‐Ping Tsai, et al.. (2021). From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?. Environmental Science & Technology. 55(4). 2357–2368. 204 indexed citations breakdown →
13.
Feng, Dapeng, Yi Zheng, Yixin Mao, et al.. (2017). An integrated hydrological modeling approach for detection and attribution of climatic and human impacts on coastal water resources. Journal of Hydrology. 557. 305–320. 42 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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