216 related articles for article (PubMed ID: 24493265)
1. Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater.
Hosseini SM; Mahjouri N
Environ Monit Assess; 2014 Jun; 186(6):3685-99. PubMed ID: 24493265
[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. Modeling of nitrate concentration in groundwater using artificial intelligence approach--a case study of Gaza coastal aquifer.
Alagha JS; Said MA; Mogheir Y
Environ Monit Assess; 2014 Jan; 186(1):35-45. PubMed ID: 23974533
[TBL] [Abstract][Full Text] [Related]
4. Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring.
Najah A; El-Shafie A; Karim OA; El-Shafie AH
Environ Sci Pollut Res Int; 2014 Feb; 21(3):1658-1670. PubMed ID: 23949111
[TBL] [Abstract][Full Text] [Related]
5. Compositional cokriging for mapping the probability risk of groundwater contamination by nitrates.
Pardo-Igúzquiza E; Chica-Olmo M; Luque-Espinar JA; Rodríguez-Galiano V
Sci Total Environ; 2015 Nov; 532():162-75. PubMed ID: 26070026
[TBL] [Abstract][Full Text] [Related]
6. Numerical modeling of groundwater flow and nitrate transport using MODFLOW and MT3DMS in the Karaj alluvial aquifer, Iran.
Shakeri R; Nassery HR; Ebadi T
Environ Monit Assess; 2022 Dec; 195(1):242. PubMed ID: 36576614
[TBL] [Abstract][Full Text] [Related]
7. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.
Nadiri AA; Gharekhani M; Khatibi R; Moghaddam AA
Environ Sci Pollut Res Int; 2017 Mar; 24(9):8562-8577. PubMed ID: 28194673
[TBL] [Abstract][Full Text] [Related]
8. A fuzzy logic approach to assess groundwater pollution levels below agricultural fields.
Muhammetoglu A; Yardimci A
Environ Monit Assess; 2006 Jul; 118(1-3):337-54. PubMed ID: 16897549
[TBL] [Abstract][Full Text] [Related]
9. Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).
Vadiati M; Rajabi Yami Z; Eskandari E; Nakhaei M; Kisi O
Environ Monit Assess; 2022 Jul; 194(9):619. PubMed ID: 35904687
[TBL] [Abstract][Full Text] [Related]
10. Improving groundwater nitrate concentration prediction using local ensemble of machine learning models.
Mahboobi H; Shakiba A; Mirbagheri B
J Environ Manage; 2023 Nov; 345():118782. PubMed ID: 37597371
[TBL] [Abstract][Full Text] [Related]
11. Combining a gravitational search algorithm, particle swarm optimization, and fuzzy rules to improve the classification performance of a feed-forward neural network.
Huang ML; Chou YC
Comput Methods Programs Biomed; 2019 Oct; 180():105016. PubMed ID: 31442736
[TBL] [Abstract][Full Text] [Related]
12. Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches.
Alkindi KM; Mukherjee K; Pandey M; Arora A; Janizadeh S; Pham QB; Anh DT; Ahmadi K
Environ Sci Pollut Res Int; 2022 Mar; 29(14):20421-20436. PubMed ID: 34735705
[TBL] [Abstract][Full Text] [Related]
13. Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.
Agbasi JC; Egbueri JC
Environ Sci Pollut Res Int; 2024 May; 31(21):30370-30398. PubMed ID: 38641692
[TBL] [Abstract][Full Text] [Related]
14. Nitrate concentration analysis and prediction in a shallow aquifer in central-eastern Tunisia using artificial neural network and time series modelling.
El Amri A; M'nassri S; Nasri N; Nsir H; Majdoub R
Environ Sci Pollut Res Int; 2022 Jun; 29(28):43300-43318. PubMed ID: 35091932
[TBL] [Abstract][Full Text] [Related]
15. Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks.
Liu P; Li H
IEEE Trans Neural Netw; 2004 May; 15(3):545-58. PubMed ID: 15384545
[TBL] [Abstract][Full Text] [Related]
16. Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models.
Rizeei HM; Azeez OS; Pradhan B; Khamees HH
Environ Monit Assess; 2018 Oct; 190(11):633. PubMed ID: 30288624
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. DCT-Yager FNN: a novel Yager-based fuzzy neural network with the discrete clustering technique.
Singh A; Quek C; Cho SY
IEEE Trans Neural Netw; 2008 Apr; 19(4):625-44. PubMed ID: 18390309
[TBL] [Abstract][Full Text] [Related]
19. Assessing the hydrogeochemical processes affecting groundwater pollution in arid areas using an integration of geochemical equilibrium and multivariate statistical techniques.
El Alfy M; Lashin A; Abdalla F; Al-Bassam A
Environ Pollut; 2017 Oct; 229():760-770. PubMed ID: 28624130
[TBL] [Abstract][Full Text] [Related]
20. Performance assessment of artificial neural networks and support vector regression models for stream flow predictions.
Ateeq-Ur-Rauf ; Ghumman AR; Ahmad S; Hashmi HN
Environ Monit Assess; 2018 Nov; 190(12):704. PubMed ID: 30406854
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]