111 related articles for article (PubMed ID: 29272835)
1. Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods.
Rodriguez-Galiano VF; Luque-Espinar JA; Chica-Olmo M; Mendes MP
Sci Total Environ; 2018 May; 624():661-672. PubMed ID: 29272835
[TBL] [Abstract][Full Text] [Related]
2. Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: a case study in an agricultural setting (Southern Spain).
Rodriguez-Galiano V; Mendes MP; Garcia-Soldado MJ; Chica-Olmo M; Ribeiro L
Sci Total Environ; 2014 Apr; 476-477():189-206. PubMed ID: 24463255
[TBL] [Abstract][Full Text] [Related]
3. Two-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseases.
Xie J; Lei J; Xie W; Shi Y; Liu X
Health Inf Sci Syst; 2013; 1():10. PubMed ID: 26042184
[TBL] [Abstract][Full Text] [Related]
4. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods.
Rahmati O; Choubin B; Fathabadi A; Coulon F; Soltani E; Shahabi H; Mollaefar E; Tiefenbacher J; Cipullo S; Ahmad BB; Tien Bui D
Sci Total Environ; 2019 Oct; 688():855-866. PubMed ID: 31255823
[TBL] [Abstract][Full Text] [Related]
5. A machine learning framework for spatio-temporal vulnerability mapping of groundwaters to nitrate in a data scarce region in Lenjanat Plain, Iran.
Jalali R; Tishehzan P; Hashemi H
Environ Sci Pollut Res Int; 2024 Jun; 31(29):42088-42110. PubMed ID: 38862797
[TBL] [Abstract][Full Text] [Related]
6. Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning.
Knoll L; Breuer L; Bach M
Sci Total Environ; 2019 Jun; 668():1317-1327. PubMed ID: 31018471
[TBL] [Abstract][Full Text] [Related]
7. A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination.
Sajedi-Hosseini F; Malekian A; Choubin B; Rahmati O; Cipullo S; Coulon F; Pradhan B
Sci Total Environ; 2018 Dec; 644():954-962. PubMed ID: 30743892
[TBL] [Abstract][Full Text] [Related]
8. A novel feature selection approach for biomedical data classification.
Peng Y; Wu Z; Jiang J
J Biomed Inform; 2010 Feb; 43(1):15-23. PubMed ID: 19647098
[TBL] [Abstract][Full Text] [Related]
9. A random forest classifier for lymph diseases.
Azar AT; Elshazly HI; Hassanien AE; Elkorany AM
Comput Methods Programs Biomed; 2014 Feb; 113(2):465-73. PubMed ID: 24290902
[TBL] [Abstract][Full Text] [Related]
10. A classification system based on a new wrapper feature selection algorithm for the diagnosis of primary and secondary polycythemia.
Korfiatis VCh; Asvestas PA; Delibasis KK; Matsopoulos GK
Comput Biol Med; 2013 Dec; 43(12):2118-26. PubMed ID: 24290929
[TBL] [Abstract][Full Text] [Related]
11. Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods.
Chen W; Tsangaratos P; Ilia I; Duan Z; Chen X
Sci Total Environ; 2019 Sep; 684():31-49. PubMed ID: 31150874
[TBL] [Abstract][Full Text] [Related]
12. GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran.
Naghibi SA; Pourghasemi HR; Dixon B
Environ Monit Assess; 2016 Jan; 188(1):44. PubMed ID: 26687087
[TBL] [Abstract][Full Text] [Related]
13. Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential.
Naghibi SA; Dolatkordestani M; Rezaei A; Amouzegari P; Heravi MT; Kalantar B; Pradhan B
Environ Monit Assess; 2019 Mar; 191(4):248. PubMed ID: 30919064
[TBL] [Abstract][Full Text] [Related]
14. Spatial assessment of animal manure spreading and groundwater nitrate pollution.
Infascelli R; Pelorosso R; Boccia L
Geospat Health; 2009 Nov; 4(1):27-38. PubMed ID: 19908188
[TBL] [Abstract][Full Text] [Related]
15. Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging.
Fu JJ; Yu YW; Lin HM; Chai JW; Chen CC
Comput Med Imaging Graph; 2014 Jun; 38(4):267-75. PubMed ID: 24495469
[TBL] [Abstract][Full Text] [Related]
16. Effect of finite sample size on feature selection and classification: a simulation study.
Way TW; Sahiner B; Hadjiiski LM; Chan HP
Med Phys; 2010 Feb; 37(2):907-20. PubMed ID: 20229900
[TBL] [Abstract][Full Text] [Related]
17. Susceptibility Assessment of Groundwater Nitrate Contamination Using an Ensemble Machine Learning Approach.
Hosseini FS; Choubin B; Bagheri-Gavkosh M; Karimi O; Taromideh F; Mako C
Ground Water; 2023; 61(4):510-516. PubMed ID: 36127852
[TBL] [Abstract][Full Text] [Related]
18. Comparative evaluation of machine learning models for groundwater quality assessment.
Bedi S; Samal A; Ray C; Snow D
Environ Monit Assess; 2020 Nov; 192(12):776. PubMed ID: 33219864
[TBL] [Abstract][Full Text] [Related]
19. [Nitrate pollution in groundwater for drinking and its affecting factors in Hailun, northeast China].
Zhao XF; Yang LR; Shi Q; Ma Y; Zhang YY; Chen LD; Zheng HF
Huan Jing Ke Xue; 2008 Nov; 29(11):2993-8. PubMed ID: 19186792
[TBL] [Abstract][Full Text] [Related]
20. [Nitrate contamination of the groundwater of the Akkar Plain in northern Lebanon].
Halwani J; Baroudi BO; Wartel M
Sante; 1999; 9(4):219-23. PubMed ID: 10623868
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
