Jiannan Luo

812 total citations
27 papers, 631 citations indexed

About

Jiannan Luo is a scholar working on Environmental Engineering, Civil and Structural Engineering and Ocean Engineering. According to data from OpenAlex, Jiannan Luo has authored 27 papers receiving a total of 631 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Environmental Engineering, 13 papers in Civil and Structural Engineering and 10 papers in Ocean Engineering. Recurrent topics in Jiannan Luo's work include Groundwater flow and contamination studies (20 papers), Water Systems and Optimization (10 papers) and Hydrological Forecasting Using AI (6 papers). Jiannan Luo is often cited by papers focused on Groundwater flow and contamination studies (20 papers), Water Systems and Optimization (10 papers) and Hydrological Forecasting Using AI (6 papers). Jiannan Luo collaborates with scholars based in China, Spain and Poland. Jiannan Luo's co-authors include Wenxi Lu, Wenxi Lu, Haibo Chu, Jiuhui Li, Xin Xin, Zeyu Hou, Qingchun Yang, Jordi Delgado Martín, Qi Ouyang and Zijun Li and has published in prestigious journals such as Journal of Hydrology, Environmental Science and Pollution Research and Environmental Research.

In The Last Decade

Jiannan Luo

27 papers receiving 626 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jiannan Luo China 17 426 154 124 114 111 27 631
Bart Rogiers Belgium 11 299 0.7× 212 1.4× 128 1.0× 76 0.7× 66 0.6× 50 574
Hugo A. Loáiciga United States 14 510 1.2× 226 1.5× 259 2.1× 180 1.6× 94 0.8× 36 720
Jiabao Guan United States 14 425 1.0× 420 2.7× 296 2.4× 260 2.3× 44 0.4× 35 807
Liang‐Cheng Chang Taiwan 14 414 1.0× 216 1.4× 411 3.3× 252 2.2× 65 0.6× 54 806
Chuanjun Zhan China 12 281 0.7× 90 0.6× 143 1.2× 95 0.8× 43 0.4× 19 577
Bülent Tütmez Türkiye 12 146 0.3× 153 1.0× 113 0.9× 72 0.6× 37 0.3× 49 548
Qiankun Luo China 10 242 0.6× 88 0.6× 108 0.9× 101 0.9× 99 0.9× 46 437
F. Delay France 11 746 1.8× 311 2.0× 231 1.9× 106 0.9× 172 1.5× 25 936
Mehdi Azhdary Moghaddam Iran 16 186 0.4× 363 2.4× 105 0.8× 129 1.1× 49 0.4× 45 798
Haibo Chu China 14 345 0.8× 55 0.4× 69 0.6× 258 2.3× 23 0.2× 29 581

Countries citing papers authored by Jiannan Luo

Since Specialization
Citations

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

Fields of papers citing papers by Jiannan Luo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jiannan Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Jiannan Luo. A scholar is included among the top collaborators of Jiannan Luo 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 Jiannan Luo. Jiannan Luo 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.
2.
Luo, Jiannan, et al.. (2023). Review of machine learning-based surrogate models of groundwater contaminant modeling. Environmental Research. 238(Pt 2). 117268–117268. 45 indexed citations
3.
Luo, Jiannan, et al.. (2022). Machine learning-based optimal design of groundwater pollution monitoring network. Environmental Research. 211. 113022–113022. 21 indexed citations
4.
Liu, Yong, et al.. (2022). Inversion of hydrogeological parameters based on an adaptive dynamic surrogate model. Hydrogeology Journal. 30(5). 1513–1527. 3 indexed citations
5.
Luo, Jiannan, et al.. (2022). Inversion of groundwater contamination source based on a two-stage adaptive surrogate model-assisted trust region genetic algorithm framework. Applied Mathematical Modelling. 112. 262–281. 16 indexed citations
6.
Lu, Wenxi, et al.. (2022). A combined search method based on a deep learning combined surrogate model for groundwater DNAPL contamination source identification. Journal of Hydrology. 616. 128854–128854. 20 indexed citations
7.
Li, Jiuhui, Wenxi Lu, & Jiannan Luo. (2021). Groundwater contamination sources identification based on the Long-Short Term Memory network. Journal of Hydrology. 601. 126670–126670. 38 indexed citations
8.
Lu, Wenxi, et al.. (2020). Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty. Environmental Science and Pollution Research. 27(19). 24090–24102. 17 indexed citations
9.
10.
Chang, Zhenbo, Wenxi Lu, Han Wang, Jiuhui Li, & Jiannan Luo. (2020). Simultaneous identification of groundwater contaminant sources and simulation of model parameters based on an improved single-component adaptive Metropolis algorithm. Hydrogeology Journal. 29(2). 859–873. 20 indexed citations
11.
Li, Zijun, Qingchun Yang, Yuesuo Yang, et al.. (2019). Isotopic and geochemical interpretation of groundwater under the influences of anthropogenic activities. Journal of Hydrology. 576. 685–697. 86 indexed citations
12.
Luo, Jiannan, et al.. (2019). Comparison of Surrogate Models Based on Different Sampling Methods for Groundwater Remediation. Journal of Water Resources Planning and Management. 145(5). 24 indexed citations
13.
Ouyang, Qi, et al.. (2017). Application of ensemble surrogates and adaptive sequential sampling to optimal groundwater remediation design at DNAPLs-contaminated sites. Journal of Contaminant Hydrology. 207. 31–38. 31 indexed citations
14.
Ouyang, Qi, Wenxi Lu, Zeyu Hou, et al.. (2017). Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method. Journal of Contaminant Hydrology. 200. 15–23. 40 indexed citations
15.
Luo, Jiannan, et al.. (2017). Optimal Latin hypercube sampling-based surrogate model in NAPLs contaminated groundwater remediation optimization process. Water Science & Technology Water Supply. 18(1). 333–346. 12 indexed citations
16.
Luo, Jiannan, et al.. (2016). A comparison of three prediction models for predicting monthly precipitation in Liaoyuan city, China. Water Science & Technology Water Supply. 16(3). 845–854. 8 indexed citations
17.
Hou, Zeyu, Wenxi Lu, Haibo Chu, & Jiannan Luo. (2015). Selecting Parameter-Optimized Surrogate Models in DNAPL-Contaminated Aquifer Remediation Strategies. Environmental Engineering Science. 32(12). 1016–1026. 42 indexed citations
18.
Luo, Jiannan & Wenxi Lu. (2014). Comparison of surrogate models with different methods in groundwater remediation process. Journal of Earth System Science. 123(7). 1579–1589. 51 indexed citations
19.
Luo, Jiannan & Wenxi Lu. (2014). A mixed-integer non-linear programming with surrogate model for optimal remediation design of NAPLs contaminated aquifer. International Journal of Environment and Pollution. 54(1). 1–1. 17 indexed citations
20.
Luo, Jiannan, Wenxi Lu, Xin Xin, & Haibo Chu. (2013). Surrogate model application to the identification of an optimal surfactant-enhanced aquifer remediation strategy for DNAPL-contaminated sites. Journal of Earth Science. 24(6). 1023–1032. 28 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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