173 related articles for article (PubMed ID: 21562791)
1. ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study.
Heddam S; Bermad A; Dechemi N
Environ Monit Assess; 2012 Apr; 184(4):1953-71. PubMed ID: 21562791
[TBL] [Abstract][Full Text] [Related]
2. Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS).
Narges S; Ghorban A; Hassan K; Mohammad K
J Environ Health Sci Eng; 2021 Dec; 19(2):1543-1553. PubMed ID: 34900287
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Dissolved oxygen modelling of the Yamuna River using different ANFIS models.
Arora S; Keshari AK
Water Sci Technol; 2021 Nov; 84(10-11):3359-3371. PubMed ID: 34850733
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm.
Adnan RM; Dai HL; Kisi O; Heddam S; Kim S; Kulls C; Zounemat-Kermani M
Environ Sci Pollut Res Int; 2023 Sep; 30(41):94312-94333. PubMed ID: 37531049
[TBL] [Abstract][Full Text] [Related]
7. Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources.
Jafarzade N; Kisi O; Yousefi M; Baziar M; Oskoei V; Marufi N; Mohammadi AA
Heliyon; 2023 Aug; 9(8):e18415. PubMed ID: 37520981
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge.
Cakmakci M
Bioprocess Biosyst Eng; 2007 Sep; 30(5):349-57. PubMed ID: 17593401
[TBL] [Abstract][Full Text] [Related]
10. Comparative predictive modelling of the occurrence of faecal indicator bacteria in a drinking water source in Norway.
Mohammed H; Hameed IA; Seidu R
Sci Total Environ; 2018 Jul; 628-629():1178-1190. PubMed ID: 30045540
[TBL] [Abstract][Full Text] [Related]
11. Optimizing the coagulation process in a drinking water treatment plant -- comparison between traditional and statistical experimental design jar tests.
Zainal-Abideen M; Aris A; Yusof F; Abdul-Majid Z; Selamat A; Omar SI
Water Sci Technol; 2012; 65(3):496-503. PubMed ID: 22258681
[TBL] [Abstract][Full Text] [Related]
12. Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning.
Stieler F; Yan H; Lohr F; Wenz F; Yin FF
Radiat Oncol; 2009 Sep; 4():39. PubMed ID: 19781059
[TBL] [Abstract][Full Text] [Related]
13. An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction.
Ahmadianfar I; Shirvani-Hosseini S; He J; Samadi-Koucheksaraee A; Yaseen ZM
Sci Rep; 2022 Mar; 12(1):4934. PubMed ID: 35322087
[TBL] [Abstract][Full Text] [Related]
14. Prediction of municipal solid waste generation: an investigation of the effect of clustering techniques and parameters on ANFIS model performance.
Adeleke O; Akinlabi SA; Jen TC; Dunmade I
Environ Technol; 2022 Apr; 43(11):1634-1647. PubMed ID: 33143558
[TBL] [Abstract][Full Text] [Related]
15. Prediction of coagulation and flocculation processes using ANN models and fuzzy regression.
Zangooei H; Delnavaz M; Asadollahfardi G
Water Sci Technol; 2016 Sep; 74(6):1296-1311. PubMed ID: 27685960
[TBL] [Abstract][Full Text] [Related]
16. Adaptive neuro-fuzzy inference system for real-time monitoring of integrated-constructed wetlands.
Dzakpasu M; Scholz M; McCarthy V; Jordan S; Sani A
Water Sci Technol; 2015; 71(1):22-30. PubMed ID: 25607665
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm.
Boumezbeur H; Laouacheria F; Heddam S; Djemili L
Environ Sci Pollut Res Int; 2023 Jun; 30(28):72463-72483. PubMed ID: 37171728
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Optimization of adaptive neuro-fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption.
Duranoğlu D; Sinan Altın E; Küçük İ
Heliyon; 2024 Feb; 10(3):e25813. PubMed ID: 38356503
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
