198 related articles for article (PubMed ID: 31377525)
1. Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network.
Zhang Y; Gao X; Smith K; Inial G; Liu S; Conil LB; Pan B
Water Res; 2019 Nov; 164():114888. PubMed ID: 31377525
[TBL] [Abstract][Full Text] [Related]
2. Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model.
Li B; Lu C; Zhao J; Tian J; Sun J; Hu C
J Environ Manage; 2023 May; 333():117416. PubMed ID: 36758403
[TBL] [Abstract][Full Text] [Related]
3. A hybrid evolutionary data driven model for river water quality early warning.
Burchard-Levine A; Liu S; Vince F; Li M; Ostfeld A
J Environ Manage; 2014 Oct; 143():8-16. PubMed ID: 24833523
[TBL] [Abstract][Full Text] [Related]
4. Hybrid artificial neural network genetic algorithm technique for modeling chemical oxygen demand removal in anoxic/oxic process.
Ma Y; Huang M; Wan J; Hu K; Wang Y; Zhang H
J Environ Sci Health A Tox Hazard Subst Environ Eng; 2011; 46(6):574-80. PubMed ID: 21500072
[TBL] [Abstract][Full Text] [Related]
5. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance.
Mjalli FS; Al-Asheh S; Alfadala HE
J Environ Manage; 2007 May; 83(3):329-38. PubMed ID: 16806660
[TBL] [Abstract][Full Text] [Related]
6. Influence of environmental variables on the energy efficiency of drinking water treatment plants.
Molinos-Senante M; Maziotis A
Sci Total Environ; 2022 Aug; 833():155246. PubMed ID: 35427615
[TBL] [Abstract][Full Text] [Related]
7. Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao Reservoir, China.
Liu Y; Xi DG; Li ZL
PLoS One; 2015; 10(3):e0119082. PubMed ID: 25768650
[TBL] [Abstract][Full Text] [Related]
8. Implementation of an environmental decision support system for controlling the pre-oxidation step at a full-scale drinking water treatment plant.
Godo-Pla L; Emiliano P; González S; Poch M; Valero F; Monclús H
Water Sci Technol; 2020 Apr; 81(8):1778-1785. PubMed ID: 32644970
[TBL] [Abstract][Full Text] [Related]
9. Prediction of COD in industrial wastewater treatment plant using an artificial neural network.
Çimen Mesutoğlu Ö; Gök O
Sci Rep; 2024 Jun; 14(1):13750. PubMed ID: 38877150
[TBL] [Abstract][Full Text] [Related]
10. A software sensor model based on hybrid fuzzy neural network for rapid estimation water quality in Guangzhou section of Pearl River, China.
Zhou C; Zhang C; Tian D; Wang K; Huang M; Liu Y
J Environ Sci Health A Tox Hazard Subst Environ Eng; 2018 Jan; 53(1):91-98. PubMed ID: 29083952
[TBL] [Abstract][Full Text] [Related]
11. A universal deep learning approach for modeling the flow of patients under different severities.
Jiang S; Chin KS; Tsui KL
Comput Methods Programs Biomed; 2018 Feb; 154():191-203. PubMed ID: 29249343
[TBL] [Abstract][Full Text] [Related]
12. A cumulative-risk assessment method based on an artificial neural network model for the water environment.
Shi E; Shang Y; Li Y; Zhang M
Environ Sci Pollut Res Int; 2021 Sep; 28(34):46176-46185. PubMed ID: 33492592
[TBL] [Abstract][Full Text] [Related]
13. Isotherm and kinetics study of malachite green adsorption onto copper nanowires loaded on activated carbon: artificial neural network modeling and genetic algorithm optimization.
Ghaedi M; Shojaeipour E; Ghaedi AM; Sahraei R
Spectrochim Acta A Mol Biomol Spectrosc; 2015 May; 142():135-49. PubMed ID: 25699703
[TBL] [Abstract][Full Text] [Related]
14. Artificial neural network modelling of a large-scale wastewater treatment plant operation.
Güçlü D; Dursun S
Bioprocess Biosyst Eng; 2010 Nov; 33(9):1051-8. PubMed ID: 20445993
[TBL] [Abstract][Full Text] [Related]
15. Water quality modelling using principal component analysis and artificial neural network.
Ibrahim A; Ismail A; Juahir H; Iliyasu AB; Wailare BT; Mukhtar M; Aminu H
Mar Pollut Bull; 2023 Feb; 187():114493. PubMed ID: 36566515
[TBL] [Abstract][Full Text] [Related]
16. A novel artificial intelligent model for predicting water treatment efficiency of various biochar systems based on artificial neural network and queuing search algorithm.
Zheng X; Nguyen H
Chemosphere; 2022 Jan; 287(Pt 3):132251. PubMed ID: 34826934
[TBL] [Abstract][Full Text] [Related]
17. Water consumption prediction and influencing factor analysis based on PCA-BP neural network in karst regions: a case study of Guizhou Province.
Yang Z; Li B; Wu H; Li M; Fan J; Chen M; Long J
Environ Sci Pollut Res Int; 2023 Mar; 30(12):33504-33515. PubMed ID: 36480138
[TBL] [Abstract][Full Text] [Related]
18. Artificial neural network (ANN) approach for modeling of Pb(II) adsorption from aqueous solution by Antep pistachio (Pistacia Vera L.) shells.
Yetilmezsoy K; Demirel S
J Hazard Mater; 2008 May; 153(3):1288-300. PubMed ID: 17980484
[TBL] [Abstract][Full Text] [Related]
19. Real-time reservoir operation using data mining techniques.
Bozorg-Haddad O; Aboutalebi M; Ashofteh PS; Loáiciga HA
Environ Monit Assess; 2018 Sep; 190(10):594. PubMed ID: 30232560
[TBL] [Abstract][Full Text] [Related]
20. Medium optimization for pyrroloquinoline quinone (PQQ) production by Methylobacillus sp. zju323 using response surface methodology and artificial neural network-genetic algorithm.
Wei P; Si Z; Lu Y; Yu Q; Huang L; Xu Z
Prep Biochem Biotechnol; 2017 Aug; 47(7):709-719. PubMed ID: 28448745
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
