Dan Lu

2.9k total citations · 1 hit paper
106 papers, 2.0k citations indexed

About

Dan Lu is a scholar working on Environmental Engineering, Water Science and Technology and Global and Planetary Change. According to data from OpenAlex, Dan Lu has authored 106 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Environmental Engineering, 36 papers in Water Science and Technology and 29 papers in Global and Planetary Change. Recurrent topics in Dan Lu's work include Hydrology and Watershed Management Studies (21 papers), Groundwater flow and contamination studies (16 papers) and Hydrological Forecasting Using AI (15 papers). Dan Lu is often cited by papers focused on Hydrology and Watershed Management Studies (21 papers), Groundwater flow and contamination studies (16 papers) and Hydrological Forecasting Using AI (15 papers). Dan Lu collaborates with scholars based in United States, China and France. Dan Lu's co-authors include Ming Ye, Scott Painter, Lin Zhang, Zhikan Yao, Shih‐Chieh Kao, Shlomo P. Neuman, Daniel Ricciuto, Goutam Konapala, Liang Xue and Guannan Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Environmental Science & Technology and The Journal of Physical Chemistry B.

In The Last Decade

Dan Lu

95 papers receiving 2.0k citations

Hit Papers

align trajectories

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.

Separation mechanism, selectivity enhancement strategies and advanced materials for mono-/multivalent ion-selective nanofiltration membrane

2022 159 citations Dan Lu et al. SHILAP Revista de lepidopterología profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dan Lu United States	26	890	776	497	369	332	106	2.0k
Jianfeng Wu China	27	736 0.8×	1.2k 1.5×	446 0.9×	215 0.6×	501 1.5×	122	2.4k
Xiaoqing Shi China	33	552 0.6×	1.1k 1.4×	242 0.5×	280 0.8×	660 2.0×	124	3.1k
Zhong Li Canada	33	1.2k 1.3×	849 1.1×	1.0k 2.1×	79 0.2×	547 1.6×	126	3.4k
Scott James United States	30	380 0.4×	665 0.9×	159 0.3×	224 0.6×	276 0.8×	127	2.5k
Shuo Wang China	34	1.0k 1.1×	525 0.7×	1.5k 3.0×	115 0.3×	570 1.7×	188	3.6k
Seong‐Joon Kim South Korea	28	1.4k 1.6×	689 0.9×	994 2.0×	80 0.2×	256 0.8×	263	2.8k
Yunwei Sun United States	25	204 0.2×	1.6k 2.1×	375 0.8×	135 0.4×	510 1.5×	91	2.4k
Jinhui Jeanne Huang‬‬‬‬ China	32	1.0k 1.1×	882 1.1×	590 1.2×	319 0.9×	136 0.4×	143	2.5k
Roberto Greco Italy	41	305 0.3×	573 0.7×	537 1.1×	277 0.8×	296 0.9×	178	4.8k
Hairong Zhang China	21	566 0.6×	485 0.6×	524 1.1×	223 0.6×	95 0.3×	93	1.9k

Countries citing papers authored by Dan Lu

Since Specialization

Citations

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

Fields of papers citing papers by Dan Lu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dan Lu

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Ullrich, Paul, Elizabeth A. Barnes, William D. Collins, et al.. (2025). Recommendations for Comprehensive and Independent Evaluation of Machine Learning‐Based Earth System Models. SHILAP Revista de lepidopterología. 2(1). 3 indexed citations

Ambika, Anukesh Krishnankutty, et al.. (2025). Novel Deep Learning Transformer Model for Short to Sub‐Seasonal Streamflow Forecast. Geophysical Research Letters. 52(14).

Lu, Dan, et al.. (2024). Developing adjustable micro- and mesopore structured carbon materials from wood via biotechnology for enhanced capacitive deionization. Chemical Engineering Journal. 500. 157043–157043. 15 indexed citations

Gao, Bo, Ethan T. Coon, Peter Thornton, & Dan Lu. (2024). Improving the estimation of atmospheric water vapor pressure using interpretable long short-term memory networks. Agricultural and Forest Meteorology. 347. 109907–109907. 5 indexed citations

Chae, K. Y., et al.. (2024). Probabilistic neural networks for improved analyses with phenomenological R-matrix. Physical review. C. 110(5). 1 indexed citations

Schram, Malachi, T. Britton, Chris H. Pappas, et al.. (2024). Distance preserving machine learning for uncertainty aware accelerator capacitance predictions. Machine Learning Science and Technology. 5(4). 45009–45009. 1 indexed citations

Lu, Dan, Yanfang Liu, Zezhong Zhang, Feng Bao, & Guannan Zhang. (2024). A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1(3). 6 indexed citations

Fan, Ming, et al.. (2024). A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage. Journal of Hydrology. 648. 132323–132323. 3 indexed citations

Liu, Siyan, Ming Fan, & Dan Lu. (2023). Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images. Geoenergy Science and Engineering. 230. 212160–212160. 6 indexed citations

10.

Topp, Simon, Alexander Y. Sun, Xiaowei Jia, et al.. (2023). Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture. Water Resources Research. 59(4). 25 indexed citations

11.

Liu, Siyan, Dan Lu, Scott Painter, Natalie A. Griffiths, & Eric M. Pierce. (2023). Uncertainty quantification of machine learning models to improve streamflow prediction under changing climate and environmental conditions. Frontiers in Water. 5. 18 indexed citations

12.

Chen, Anping, Daniel Ricciuto, Jiafu Mao, et al.. (2023). Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling. Journal of Advances in Modeling Earth Systems. 15(4). 5 indexed citations

13.

Hao, Yiqing, Erxi Feng, Dan Lu, et al.. (2023). Machine-learning-assisted automation of single-crystal neutron diffraction. Journal of Applied Crystallography. 56(2). 519–525. 6 indexed citations

14.

Lu, Dan, et al.. (2023). Multiple Unmanned Aerial Vehicle Autonomous Path Planning Algorithm Based on Whale-Inspired Deep Q-Network. Drones. 7(9). 572–572. 10 indexed citations

15.

Zhang, Guoyin, et al.. (2023). Adversarial Defense Method Based on Latent Representation Guidance for Remote Sensing Image Scene Classification. Entropy. 25(9). 1306–1306. 2 indexed citations

16.

Evans, Katherine J., Joseph H. Kennedy, Dan Lu, et al.. (2019). LIVVkit 2.1: automated and extensible ice sheet model validation. Geoscientific model development. 12(3). 1067–1086. 2 indexed citations

17.

Mo, Shaoxing, Dan Lu, Xiaoqing Shi, et al.. (2017). A Taylor Expansion‐Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling. Water Resources Research. 53(12). 10802–10823. 52 indexed citations

18.

Lu, Dan, Guannan Zhang, Clayton Webster, & Charlotte Barbier. (2016). An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations. Water Resources Research. 52(12). 9642–9660. 26 indexed citations

19.

Lu, Dan, et al.. (2014). Multilevel Monte Carlo Method with Application to Uncertainty Quantification in Reservoir Simulation. 2014 AGU Fall Meeting. 2014. 1 indexed citations

20.

Shi, Xiaoling, et al.. (2011). Assessment of Parametric Uncertainty in Groundwater Reactive Transport Modeling Using Markov Chain Monte Carlo Techniques. AGUFM. 2011.

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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