281 related articles for article (PubMed ID: 32705560)
1. 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]
2. 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]
3. 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]
4. Combining spatial autocorrelation with artificial intelligence models to estimate spatial distribution and risks of heavy metal pollution in agricultural soils.
Günal E; Budak M; Kılıç M; Cemek B; Sırrı M
Environ Monit Assess; 2023 Jan; 195(2):317. PubMed ID: 36680597
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. 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]
8. 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]
9. Prediction of oxidation parameters of purified Kilka fish oil including gallic acid and methyl gallate by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network.
Asnaashari M; Farhoosh R; Farahmandfar R
J Sci Food Agric; 2016 Oct; 96(13):4594-602. PubMed ID: 26909668
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. An investigation on environmental pollution due to essential heavy metals: a prediction model through multilayer perceptrons.
Sari M; Yalcin IE; Taner M; Cosgun T; Ozyigit II
Int J Phytoremediation; 2023; 25(1):89-97. PubMed ID: 35400247
[TBL] [Abstract][Full Text] [Related]
12. Adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) modelling of Cu, Cd, and Pb adsorption onto tropical soils.
Agbaogun BK; Olu-Owolabi BI; Buddenbaum H; Fischer K
Environ Sci Pollut Res Int; 2023 Mar; 30(11):31085-31101. PubMed ID: 36441330
[TBL] [Abstract][Full Text] [Related]
13. Evaluation of multilayer perceptron neural networks and adaptive neuro-fuzzy inference systems for the mass transfer modeling of Echium amoenum Fisch. & C. A. Mey.
Chasiotis V; Nadi F; Filios A
J Sci Food Agric; 2021 Dec; 101(15):6514-6524. PubMed ID: 34000064
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Assessment of heavy metal pollutants accumulation in the Tisza river sediments.
Sakan SM; Dordević DS; Manojlović DD; Predrag PS
J Environ Manage; 2009 Aug; 90(11):3382-90. PubMed ID: 19515481
[TBL] [Abstract][Full Text] [Related]
16. [Spatial distribution and ecological risk assessment of heavy metals in the estuaries surface sediments from the Haihe River Basin].
Lü SC; Zhang H; Shan BQ; Li LQ
Huan Jing Ke Xue; 2013 Nov; 34(11):4204-10. PubMed ID: 24455925
[TBL] [Abstract][Full Text] [Related]
17. Granular computing-neural network model for prediction of longitudinal dispersion coefficients in rivers.
Ghiasi B; Sheikhian H; Zeynolabedin A; Niksokhan MH
Water Sci Technol; 2019 Nov; 80(10):1880-1892. PubMed ID: 32144220
[TBL] [Abstract][Full Text] [Related]
18. Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production.
Taheri E; Amin MM; Fatehizadeh A; Rezakazemi M; Aminabhavi TM
J Environ Manage; 2021 Aug; 292():112759. PubMed ID: 33984638
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Fractional distribution and risk assessment of heavy metals in sediments collected from the Yellow River, China.
Liu H; Liu G; Wang J; Yuan Z; Da C
Environ Sci Pollut Res Int; 2016 Jun; 23(11):11076-11084. PubMed ID: 26906005
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]