244 related articles for article (PubMed ID: 34069195)
1. The Use of Artificial Neural Networks to Predict the Physicochemical Characteristics of Water Quality in Three District Municipalities, Eastern Cape Province, South Africa.
Setshedi KJ; Mutingwende N; Ngqwala NP
Int J Environ Res Public Health; 2021 May; 18(10):. PubMed ID: 34069195
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Predicting reservoir sedimentation using multilayer perceptron - Artificial neural network model with measured and forecasted hydrometeorological data in Gibe-III reservoir, Omo-Gibe River basin, Ethiopia.
Lukas P; Melesse AM; Kenea TT
J Environ Manage; 2024 May; 359():121018. PubMed ID: 38714033
[TBL] [Abstract][Full Text] [Related]
4. Coastal groundwater quality prediction using objective-weighted WQI and machine learning approach.
Das CR; Das S
Environ Sci Pollut Res Int; 2024 Mar; 31(13):19439-19457. PubMed ID: 38355860
[TBL] [Abstract][Full Text] [Related]
5. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation.
Jassim MS; Coskuner G; Zontul M
Waste Manag Res; 2022 Feb; 40(2):195-204. PubMed ID: 33818220
[TBL] [Abstract][Full Text] [Related]
6. Modeling oxygen and organic matter concentration in the intensive rainbow trout (Oncorhynchus mykiss) rearing system.
Galezan FH; Bayati MR; Safari O; Rohani A
Environ Monit Assess; 2020 Mar; 192(4):223. PubMed ID: 32152844
[TBL] [Abstract][Full Text] [Related]
7. Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes.
Coskuner G; Jassim MS; Zontul M; Karateke S
Waste Manag Res; 2021 Mar; 39(3):499-507. PubMed ID: 32586206
[TBL] [Abstract][Full Text] [Related]
8. Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique.
Ghritlahre HK; Prasad RK
J Environ Manage; 2018 Oct; 223():566-575. PubMed ID: 29975883
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids.
Benyekhlef A; Mohammedi B; Hassani D; Hanini S
Water Sci Technol; 2021 Aug; 84(3):538-551. PubMed ID: 34388118
[TBL] [Abstract][Full Text] [Related]
11. Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression.
Wawrzyniak J; Rudzińska M; Gawrysiak-Witulska M; Przybył K
Molecules; 2022 Apr; 27(8):. PubMed ID: 35458643
[TBL] [Abstract][Full Text] [Related]
12. Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill.
Abunama T; Othman F; Ansari M; El-Shafie A
Environ Sci Pollut Res Int; 2019 Feb; 26(4):3368-3381. PubMed ID: 30511225
[TBL] [Abstract][Full Text] [Related]
13. Prediction of force measurements of a microbend sensor based on an artificial neural network.
Efendioglu HS; Yildirim T; Fidanboylu K
Sensors (Basel); 2009; 9(9):7167-76. PubMed ID: 22399991
[TBL] [Abstract][Full Text] [Related]
14. Combining data-intelligent algorithms for the assessment and predictive modeling of groundwater resources quality in parts of southeastern Nigeria.
Egbueri JC; Agbasi JC
Environ Sci Pollut Res Int; 2022 Aug; 29(38):57147-57171. PubMed ID: 35349055
[TBL] [Abstract][Full Text] [Related]
15. An artificial neural network to model response of a radiotherapy beam monitoring system.
Cho YB; Farrokhkish M; Norrlinger B; Heaton R; Jaffray D; Islam M
Med Phys; 2020 Apr; 47(4):1983-1994. PubMed ID: 31955428
[TBL] [Abstract][Full Text] [Related]
16. Artificial neural networks and adaptive neuro-fuzzy inference systems for prediction of soil respiration in forested areas southern Brazil.
Vicentini ME; da Silva PA; Canteral KFF; De Lucena WB; de Moraes MLT; Montanari R; Filho MCMT; Peruzzi NJ; La Scala N; De Souza Rolim G; Panosso AR
Environ Monit Assess; 2023 Aug; 195(9):1074. PubMed ID: 37615714
[TBL] [Abstract][Full Text] [Related]
17. Rainfall prediction using multiple inclusive models and large climate indices.
Mohamadi S; Sheikh Khozani Z; Ehteram M; Ahmed AN; El-Shafie A
Environ Sci Pollut Res Int; 2022 Dec; 29(56):85312-85349. PubMed ID: 35790639
[TBL] [Abstract][Full Text] [Related]
18. Prediction of soil water contents and erodibility indices based on artificial neural networks: using topography and remote sensing.
Usta A
Environ Monit Assess; 2022 Sep; 194(10):794. PubMed ID: 36109443
[TBL] [Abstract][Full Text] [Related]
19. Investigating machine learning models in predicting lake water quality parameters as a 3-year moving average.
Gorgan-Mohammadi F; Rajaee T; Zounemat-Kermani M
Environ Sci Pollut Res Int; 2023 May; 30(23):63839-63863. PubMed ID: 37059948
[TBL] [Abstract][Full Text] [Related]
20. Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.
Ehteram M; Panahi F; Ahmed AN; Huang YF; Kumar P; Elshafie A
Environ Sci Pollut Res Int; 2022 Feb; 29(7):10675-10701. PubMed ID: 34528189
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]