113 related articles for article (PubMed ID: 33562175)
1. Machine Learning and Simulation-Optimization Coupling for Water Distribution Network Contamination Source Detection.
Grbčić L; Kranjčević L; Družeta S
Sensors (Basel); 2021 Feb; 21(4):. PubMed ID: 33562175
[TBL] [Abstract][Full Text] [Related]
2. A Machine Learning-based Algorithm for Water Network Contamination Source Localization.
Grbčić L; Lučin I; Kranjčević L; Družeta S
Sensors (Basel); 2020 May; 20(9):. PubMed ID: 32375289
[TBL] [Abstract][Full Text] [Related]
3. Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network.
Lučin I; Grbčić L; Čarija Z; Kranjčević L
Sensors (Basel); 2021 Jan; 21(1):. PubMed ID: 33401513
[TBL] [Abstract][Full Text] [Related]
4. Computationally efficient approach for identification of fuzzy dynamic groundwater sampling network.
Kumari K; Jain S; Dhar A
Environ Monit Assess; 2019 Apr; 191(5):310. PubMed ID: 31030264
[TBL] [Abstract][Full Text] [Related]
5. Contamination source identification in water distribution networks using convolutional neural network.
Sun L; Yan H; Xin K; Tao T
Environ Sci Pollut Res Int; 2019 Dec; 26(36):36786-36797. PubMed ID: 31745764
[TBL] [Abstract][Full Text] [Related]
6. Integrating fuzzy logic with Pearson correlation to optimize contaminant detection in water distribution system with uncertainty analyses.
Osmani SA; Banik BK; Ali H
Environ Monit Assess; 2019 Jun; 191(7):441. PubMed ID: 31203453
[TBL] [Abstract][Full Text] [Related]
7. Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network.
Buras MP; Solano Donado F
Sensors (Basel); 2021 May; 21(10):. PubMed ID: 34069087
[TBL] [Abstract][Full Text] [Related]
8. Real-time identification of indoor pollutant source positions based on neural network locator of contaminant sources and optimized sensor networks.
Vukovic V; Tabares-Velasco PC; Srebric J
J Air Waste Manag Assoc; 2010 Sep; 60(9):1034-48. PubMed ID: 20863049
[TBL] [Abstract][Full Text] [Related]
9. Selecting the best location of water quality sensors in water distribution networks by considering the importance of nodes and contaminations using NSGA-III (case study: Zahedan water distribution network, Iran).
Harif S; Azizyan G; Dehghani Darmian M; Givehchi M
Environ Sci Pollut Res Int; 2023 Apr; 30(18):53229-53252. PubMed ID: 36853532
[TBL] [Abstract][Full Text] [Related]
10. Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere.
Ma D; Zhang Z
J Hazard Mater; 2016 Jul; 311():237-45. PubMed ID: 27035273
[TBL] [Abstract][Full Text] [Related]
11. Contaminant source identification using semi-supervised machine learning.
Vesselinov VV; Alexandrov BS; O'Malley D
J Contam Hydrol; 2018 May; 212():134-142. PubMed ID: 29174719
[TBL] [Abstract][Full Text] [Related]
12. Pollution source localization in an urban water supply network based on dynamic water demand.
Yan X; Zhu Z; Li T
Environ Sci Pollut Res Int; 2019 Jun; 26(18):17901-17910. PubMed ID: 29079984
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network.
Liu H; Deng Q; Tian S; Peng X; Pei T
Sensors (Basel); 2018 Jul; 18(7):. PubMed ID: 29996557
[TBL] [Abstract][Full Text] [Related]
15. Gated graph neural networks for identifying contamination sources in water distribution systems.
Li Z; Liu H; Zhang C; Fu G
J Environ Manage; 2024 Feb; 351():119806. PubMed ID: 38118345
[TBL] [Abstract][Full Text] [Related]
16. A robust approach for iterative contaminant source location and release history recovery.
Sun AY; Painter SL; Wittmeyer GW
J Contam Hydrol; 2006 Dec; 88(3-4):181-96. PubMed ID: 16889871
[TBL] [Abstract][Full Text] [Related]
17. An almost-parameter-free harmony search algorithm for groundwater pollution source identification.
Jiang S; Zhang Y; Wang P; Zheng M
Water Sci Technol; 2013; 68(11):2359-66. PubMed ID: 24334883
[TBL] [Abstract][Full Text] [Related]
18. Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization.
Alexandridis A; Chondrodima E; Sarimveis H
IEEE Trans Neural Netw Learn Syst; 2013 Feb; 24(2):219-30. PubMed ID: 24808277
[TBL] [Abstract][Full Text] [Related]
19. Nonnegative tensor factorization for contaminant source identification.
Vesselinov VV; Alexandrov BS; O'Malley D
J Contam Hydrol; 2019 Jan; 220():66-97. PubMed ID: 30528243
[TBL] [Abstract][Full Text] [Related]
20. A Data-Based Framework for Identifying a Source Location of a Contaminant Spill in a River System with Random Measurement Errors.
Kim JH; Lee ML; Park C
Sensors (Basel); 2019 Aug; 19(15):. PubMed ID: 31374862
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
