142 related articles for article (PubMed ID: 37484325)
1. Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review.
Nawab F; Abd Hamid AS; Ibrahim A; Sopian K; Fazlizan A; Fauzan MF
Heliyon; 2023 Jun; 9(6):e17038. PubMed ID: 37484325
[TBL] [Abstract][Full Text] [Related]
2. Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study.
Patel D; Patel S; Patel P; Shah M
Environ Sci Pollut Res Int; 2022 May; 29(22):32428-32442. PubMed ID: 35178628
[TBL] [Abstract][Full Text] [Related]
3. Fuzzy-based prediction of solar PV and wind power generation for microgrid modeling using particle swarm optimization.
Teferra DM; Ngoo LMH; Nyakoe GN
Heliyon; 2023 Jan; 9(1):e12802. PubMed ID: 36704286
[TBL] [Abstract][Full Text] [Related]
4. Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.
Tikhamarine Y; Malik A; Souag-Gamane D; Kisi O
Environ Sci Pollut Res Int; 2020 Aug; 27(24):30001-30019. PubMed ID: 32445152
[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. 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]
7. Integration of artificial intelligence methods and life cycle assessment to predict energy output and environmental impacts of paddy production.
Nabavi-Pelesaraei A; Rafiee S; Mohtasebi SS; Hosseinzadeh-Bandbafha H; Chau KW
Sci Total Environ; 2018 Aug; 631-632():1279-1294. PubMed ID: 29727952
[TBL] [Abstract][Full Text] [Related]
8. Predicting hybrid rice performance using AIHIB model based on artificial intelligence.
Sabouri H; Sajadi SJ
Sci Rep; 2022 Jun; 12(1):9709. PubMed ID: 35690641
[TBL] [Abstract][Full Text] [Related]
9. A comprehensive review of solar irradiation estimation and forecasting using artificial neural networks: data, models and trends.
El-Amarty N; Marzouq M; El Fadili H; Bennani SD; Ruano A
Environ Sci Pollut Res Int; 2023 Jan; 30(3):5407-5439. PubMed ID: 36424486
[TBL] [Abstract][Full Text] [Related]
10. A review of recent developments in the application of machine learning in solar thermal collector modelling.
Vakili M; Salehi SA
Environ Sci Pollut Res Int; 2023 Jan; 30(2):2406-2439. PubMed ID: 36399296
[TBL] [Abstract][Full Text] [Related]
11. Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives.
Li C; Guo D; Dang Y; Sun D; Li P
J Environ Manage; 2023 Oct; 344():118502. PubMed ID: 37390578
[TBL] [Abstract][Full Text] [Related]
12. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP.
Jalal FE; Xu Y; Iqbal M; Javed MF; Jamhiri B
J Environ Manage; 2021 Jul; 289():112420. PubMed ID: 33831756
[TBL] [Abstract][Full Text] [Related]
13. Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals.
Bamisile O; Cai D; Oluwasanmi A; Ejiyi C; Ukwuoma CC; Ojo O; Mukhtar M; Huang Q
Sci Rep; 2022 Jun; 12(1):9644. PubMed ID: 35688900
[TBL] [Abstract][Full Text] [Related]
14. Real-Time Prediction of Rate of Penetration in S-Shape Well Profile Using Artificial Intelligence Models.
Elkatatny S
Sensors (Basel); 2020 Jun; 20(12):. PubMed ID: 32575868
[TBL] [Abstract][Full Text] [Related]
15. A state of art review on estimation of solar radiation with various models.
Gürel AE; Ağbulut Ü; Bakır H; Ergün A; Yıldız G
Heliyon; 2023 Feb; 9(2):e13167. PubMed ID: 36747538
[TBL] [Abstract][Full Text] [Related]
16. A comparative study on daily evapotranspiration estimation by using various artificial intelligence techniques and traditional regression calculations.
Güzel H; Üneş F; Erginer M; Kaya YZ; Taşar B; Erginer İ; Demirci M
Math Biosci Eng; 2023 Apr; 20(6):11328-11352. PubMed ID: 37322984
[TBL] [Abstract][Full Text] [Related]
17. Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae.
Chong JWR; Tang DYY; Leong HY; Khoo KS; Show PL; Chew KW
Bioengineered; 2023 Dec; 14(1):2244232. PubMed ID: 37578162
[TBL] [Abstract][Full Text] [Related]
18. Predicting coagulation-flocculation process for turbidity removal from water using graphene oxide: a comparative study on ANN, SVR, ANFIS, and RSM models.
Ghasemi M; Hasani Zonoozi M; Rezania N; Saadatpour M
Environ Sci Pollut Res Int; 2022 Oct; 29(48):72839-72852. PubMed ID: 35616836
[TBL] [Abstract][Full Text] [Related]
19. Estimation of thermophysical property of hybrid nanofluids for solar Thermal applications: Implementation of novel Optimizable Gaussian Process regression (O-GPR) approach for Viscosity prediction.
Adun H; Wole-Osho I; Okonkwo EC; Ruwa T; Agwa T; Onochie K; Ukwu H; Bamisile O; Dagbasi M
Neural Comput Appl; 2022; 34(13):11233-11254. PubMed ID: 35291505
[TBL] [Abstract][Full Text] [Related]
20. Power optimization of a photovoltaic system with artificial intelligence algorithms over two seasons in tropical area.
Ba A; Ndiaye A; Ndiaye EHM; Mbodji S
MethodsX; 2023; 10():101959. PubMed ID: 36545542
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]