These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
238 related articles for article (PubMed ID: 30473396)
1. The application of 0-1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source. Guo JY; Lu WX; Yang QC; Miao TS J Contam Hydrol; 2019 Jan; 220():18-25. PubMed ID: 30473396 [TBL] [Abstract][Full Text] [Related]
2. A Kriging surrogate model coupled in simulation-optimization approach for identifying release history of groundwater sources. Zhao Y; Lu W; Xiao C J Contam Hydrol; 2016; 185-186():51-60. PubMed ID: 26826982 [TBL] [Abstract][Full Text] [Related]
3. Optimal design of groundwater pollution monitoring network based on a back-propagation neural network surrogate model and grey wolf optimizer algorithm under uncertainty. Guo X; Luo J; Lu W; Dong G; Pan Z Environ Monit Assess; 2024 Jan; 196(2):132. PubMed ID: 38200367 [TBL] [Abstract][Full Text] [Related]
4. Identification of groundwater contamination sources and hydraulic parameters based on bayesian regularization deep neural network. Pan Z; Lu W; Fan Y; Li J Environ Sci Pollut Res Int; 2021 Apr; 28(13):16867-16879. PubMed ID: 33398760 [TBL] [Abstract][Full Text] [Related]
5. Simultaneous identification of groundwater contaminant source and hydraulic parameters based on multilayer perceptron and flying foxes optimization. Li Y; Lu W; Pan Z; Wang Z; Dong G Environ Sci Pollut Res Int; 2023 Jul; 30(32):78933-78947. PubMed ID: 37277589 [TBL] [Abstract][Full Text] [Related]
6. Recognition of a linear source contamination based on a mixed-integer stacked chaos gate recurrent unit neural network-hybrid sparrow search algorithm. Pan Z; Lu W; Wang H; Bai Y Environ Sci Pollut Res Int; 2022 May; 29(22):33528-33543. PubMed ID: 35029835 [TBL] [Abstract][Full Text] [Related]
7. Machine learning-based optimal design of groundwater pollution monitoring network. Xiong Y; Luo J; Liu X; Liu Y; Xin X; Wang S Environ Res; 2022 Aug; 211():113022. PubMed ID: 35278471 [TBL] [Abstract][Full Text] [Related]
8. A construction strategy for conservative adaptive Kriging surrogate model with application in the optimal design of contaminated groundwater extraction-treatment. Zhang S; Qiang J; Liu H; Zhu X; Lv H Environ Sci Pollut Res Int; 2022 Jun; 29(28):42792-42808. PubMed ID: 35088275 [TBL] [Abstract][Full Text] [Related]
9. Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method-A Case Study of Western Jilin Province. An Y; Lu W; Cheng W Int J Environ Res Public Health; 2015 Jul; 12(8):8897-918. PubMed ID: 26264008 [TBL] [Abstract][Full Text] [Related]
10. Groundwater contamination sources identification based on kernel extreme learning machine and its effect due to wavelet denoising technique. Li J; Lu W; Wang H; Bai Y; Fan Y Environ Sci Pollut Res Int; 2020 Sep; 27(27):34107-34120. PubMed ID: 32557044 [TBL] [Abstract][Full Text] [Related]
11. Groundwater contamination source identification based on Sobol sequences-based sparrow search algorithm with a BiLSTM surrogate model. Ge Y; Lu W; Pan Z Environ Sci Pollut Res Int; 2023 Apr; 30(18):53191-53203. PubMed ID: 36854941 [TBL] [Abstract][Full Text] [Related]
12. Identification of clandestine groundwater pollution sources using heuristics optimization algorithms: a comparison between simulated annealing and particle swarm optimization. Chakraborty A; Prakash O Environ Monit Assess; 2020 Nov; 192(12):791. PubMed ID: 33242155 [TBL] [Abstract][Full Text] [Related]
13. Optimal layout design of groundwater pollution monitoring network using parameter iterative updating strategy-based ant colony optimization algorithm. Luo J; Xiong Y; Song Z; Ji Y; Xin X; Zou H Environ Sci Pollut Res Int; 2023 Nov; 30(53):114535-114555. PubMed ID: 37861835 [TBL] [Abstract][Full Text] [Related]
14. Identification of light nonaqueous phase liquid groundwater contamination source based on empirical mode decomposition and deep learning. Li J; Wu Z; He H; Lu W Environ Sci Pollut Res Int; 2023 Mar; 30(13):38663-38682. PubMed ID: 36585581 [TBL] [Abstract][Full Text] [Related]
15. A construction strategy of Kriging surrogate model based on Rosenblatt transformation of associated random variables and its application in groundwater remediation. Qiang J; Zhang S; Liu H; Zhu X; Zhou J J Environ Manage; 2024 Jan; 349():119555. PubMed ID: 37980793 [TBL] [Abstract][Full Text] [Related]
16. Simultaneous identification of groundwater pollution source and important hydrogeological parameters considering the noise uncertainty of observational data. Luo C; Lu W; Pan Z; Bai Y; Dong G Environ Sci Pollut Res Int; 2023 Jul; 30(35):84267-84282. PubMed ID: 37365362 [TBL] [Abstract][Full Text] [Related]
17. Optimal design of groundwater pollution monitoring network based on the SVR surrogate model under uncertainty. Fan Y; Lu W; Miao T; An Y; Li J; Luo J Environ Sci Pollut Res Int; 2020 Jul; 27(19):24090-24102. PubMed ID: 32304051 [TBL] [Abstract][Full Text] [Related]
18. Source Characterization of Multiple Reactive Species at an Abandoned Mine Site Using a Groundwater Numerical Simulation Model and Optimization Models. Hayford MS; Datta B Int J Environ Res Public Health; 2021 Apr; 18(9):. PubMed ID: 33947139 [TBL] [Abstract][Full Text] [Related]
19. Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method. Ouyang Q; Lu W; Hou Z; Zhang Y; Li S; Luo J J Contam Hydrol; 2017 May; 200():15-23. PubMed ID: 28363342 [TBL] [Abstract][Full Text] [Related]
20. Multi-stage optimal design for groundwater remediation: a hybrid bi-level programming approach. Zou Y; Huang GH; He L; Li H J Contam Hydrol; 2009 Aug; 108(1-2):64-76. PubMed ID: 19559499 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]