417 related articles for article (PubMed ID: 29892912)
1. Comparison of different heuristic and decomposition techniques for river stage modeling.
Seo Y; Kim S; Singh VP
Environ Monit Assess; 2018 Jun; 190(7):392. PubMed ID: 29892912
[TBL] [Abstract][Full Text] [Related]
2. A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States.
Olyaie E; Banejad H; Chau KW; Melesse AM
Environ Monit Assess; 2015 Apr; 187(4):189. PubMed ID: 25787167
[TBL] [Abstract][Full Text] [Related]
3. Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models.
Seifi A; Riahi-Madvar H
Environ Sci Pollut Res Int; 2019 Jan; 26(1):867-885. PubMed ID: 30415370
[TBL] [Abstract][Full Text] [Related]
4. Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.
Zhu S; Heddam S; Nyarko EK; Hadzima-Nyarko M; Piccolroaz S; Wu S
Environ Sci Pollut Res Int; 2019 Jan; 26(1):402-420. PubMed ID: 30406582
[TBL] [Abstract][Full Text] [Related]
5. Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel.
Wong YJ; Arumugasamy SK; Chung CH; Selvarajoo A; Sethu V
Environ Monit Assess; 2020 Jun; 192(7):439. PubMed ID: 32556670
[TBL] [Abstract][Full Text] [Related]
6. Application of adaptive neuro-fuzzy inference system for epileptic seizure detection using wavelet feature extraction.
Subasi A
Comput Biol Med; 2007 Feb; 37(2):227-44. PubMed ID: 16480706
[TBL] [Abstract][Full Text] [Related]
7. Two hybrid data-driven models for modeling water-air temperature relationship in rivers.
Zhu S; Hadzima-Nyarko M; Gao A; Wang F; Wu J; Wu S
Environ Sci Pollut Res Int; 2019 Apr; 26(12):12622-12630. PubMed ID: 30895536
[TBL] [Abstract][Full Text] [Related]
8. Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model.
Nazir HM; Hussain I; Faisal M; Elashkar EE; Shoukry AM
PeerJ; 2019; 7():e8043. PubMed ID: 31871832
[TBL] [Abstract][Full Text] [Related]
9. Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN.
Şahin M; Erol R
Comput Intell Neurosci; 2018; 2018():5714872. PubMed ID: 30158960
[TBL] [Abstract][Full Text] [Related]
10. Application of artificial neural networks to predict the heavy metal contamination in the Bartin River.
Ucun Ozel H; Gemici BT; Gemici E; Ozel HB; Cetin M; Sevik H
Environ Sci Pollut Res Int; 2020 Dec; 27(34):42495-42512. PubMed ID: 32705560
[TBL] [Abstract][Full Text] [Related]
11. Adaptive neuro-fuzzy inference system (ANFIS): a new approach to predictive modeling in QSAR applications: a study of neuro-fuzzy modeling of PCP-based NMDA receptor antagonists.
Buyukbingol E; Sisman A; Akyildiz M; Alparslan FN; Adejare A
Bioorg Med Chem; 2007 Jun; 15(12):4265-82. PubMed ID: 17434739
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance.
Sada SO; Ikpeseni SC
Heliyon; 2021 Feb; 7(2):e06136. PubMed ID: 33553780
[TBL] [Abstract][Full Text] [Related]
13. Prediction of biochemical oxygen demand at the upstream catchment of a reservoir using adaptive neuro fuzzy inference system.
Chiu YC; Chiang CW; Lee TY
Water Sci Technol; 2017 Oct; 76(7-8):1739-1753. PubMed ID: 28991790
[TBL] [Abstract][Full Text] [Related]
14. A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.
Zhang X; Zhang Q; Zhang G; Nie Z; Gui Z; Que H
Int J Environ Res Public Health; 2018 May; 15(5):. PubMed ID: 29883381
[TBL] [Abstract][Full Text] [Related]
15. Performance evaluation of artificial intelligence paradigms-artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction.
Tabbussum R; Dar AQ
Environ Sci Pollut Res Int; 2021 May; 28(20):25265-25282. PubMed ID: 33453033
[TBL] [Abstract][Full Text] [Related]
16. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition.
Wang WC; Chau KW; Qiu L; Chen YB
Environ Res; 2015 May; 139():46-54. PubMed ID: 25684671
[TBL] [Abstract][Full Text] [Related]
17. Modelling monthly mean air temperature using artificial neural network, adaptive neuro-fuzzy inference system and support vector regression methods: A case of study for Turkey.
Yakut E; Süzülmüş S
Network; 2020; 31(1-4):1-36. PubMed ID: 32397767
[TBL] [Abstract][Full Text] [Related]
18. Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study.
Heddam S
Environ Monit Assess; 2014 Jan; 186(1):597-619. PubMed ID: 24057665
[TBL] [Abstract][Full Text] [Related]
19. Ensemble adaptive network-based fuzzy inference system with weighted arithmetical mean and application to diagnosis of optic nerve disease from visual-evoked potential signals.
Akdemir B; Kara S; Polat K; Güven A; Güneş S
Artif Intell Med; 2008 Jun; 43(2):141-9. PubMed ID: 18468871
[TBL] [Abstract][Full Text] [Related]
20. Evaluating the potential of artificial neural network and neuro-fuzzy techniques for estimating antioxidant activity and anthocyanin content of sweet cherry during ripening by using image processing.
Taghadomi-Saberi S; Omid M; Emam-Djomeh Z; Ahmadi H
J Sci Food Agric; 2014 Jan; 94(1):95-101. PubMed ID: 23633396
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]