204 related articles for article (PubMed ID: 29727952)
1. 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]
2. Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production.
Kaab A; Sharifi M; Mobli H; Nabavi-Pelesaraei A; Chau KW
Sci Total Environ; 2019 May; 664():1005-1019. PubMed ID: 30769303
[TBL] [Abstract][Full Text] [Related]
3. Life cycle assessment of rice production systems in different paddy field size levels in north of Iran.
Habibi E; Niknejad Y; Fallah H; Dastan S; Tari DB
Environ Monit Assess; 2019 Mar; 191(4):202. PubMed ID: 30826990
[TBL] [Abstract][Full Text] [Related]
4. Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.
Khanali M; Mobli H; Hosseinzadeh-Bandbafha H
Environ Sci Pollut Res Int; 2017 Dec; 24(34):26324-26340. PubMed ID: 28965294
[TBL] [Abstract][Full Text] [Related]
5. Evaluation of traditional and consolidated rice farms in Guilan Province, Iran, using life cycle assessment and fuzzy modeling.
Khoshnevisan B; Rajaeifar MA; Clark S; Shamahirband S; Anuar NB; Mohd Shuib NL; Gani A
Sci Total Environ; 2014 May; 481():242-51. PubMed ID: 24602908
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Rice single cropping or ratooning agro-system: which one is more environment-friendly?
Firouzi S; Nikkhah A; Aminpanah H
Environ Sci Pollut Res Int; 2018 Nov; 25(32):32246-32256. PubMed ID: 30225691
[TBL] [Abstract][Full Text] [Related]
9. Multiyear life energy and life cycle assessment of orange production in Iran.
Alishah A; Motevali A; Tabatabaeekoloor R; Hashemi SJ
Environ Sci Pollut Res Int; 2019 Nov; 26(31):32432-32445. PubMed ID: 31612415
[TBL] [Abstract][Full Text] [Related]
10. Total environmental impacts of biofuels from corn stover using a hybrid life cycle assessment model combining process life cycle assessment and economic input-output life cycle assessment.
Liu C; Huang Y; Wang X; Tai Y; Liu L; Liu H
Integr Environ Assess Manag; 2018 Jan; 14(1):139-149. PubMed ID: 28796442
[TBL] [Abstract][Full Text] [Related]
11. Multidimensional analysis of environmental impacts from potato agricultural production in the Peruvian Central Andes.
Grados D; Schrevens E
Sci Total Environ; 2019 May; 663():927-934. PubMed ID: 30739860
[TBL] [Abstract][Full Text] [Related]
12. Environmental impacts of innovative dairy farming systems aiming at improved internal nutrient cycling: A multi-scale assessment.
de Vries W; Kros J; Dolman MA; Vellinga TV; de Boer HC; Gerritsen AL; Sonneveld MPW; Bouma J
Sci Total Environ; 2015 Dec; 536():432-442. PubMed ID: 26231773
[TBL] [Abstract][Full Text] [Related]
13. Application of analytic hierarchy process to develop a weighting scheme for life cycle assessment of agricultural production.
Nikkhah A; Firouzi S; El Haj Assad M; Ghnimi S
Sci Total Environ; 2019 May; 665():538-545. PubMed ID: 30776625
[TBL] [Abstract][Full Text] [Related]
14. Life cycle assessment of first-generation biofuels using a nitrogen crop model.
Gallejones P; Pardo G; Aizpurua A; del Prado A
Sci Total Environ; 2015 Feb; 505():1191-201. PubMed ID: 25461117
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Prediction of environmental indicators in land leveling using artificial intelligence techniques.
Alzoubi I; Delavar MR; Mirzaei F; Arrabi BN
J Environ Health Sci Eng; 2018 Jun; 16(1):65-80. PubMed ID: 30258643
[TBL] [Abstract][Full Text] [Related]
18. Projecting life-cycle environmental impacts of corn production in the U.S. Midwest under future climate scenarios using a machine learning approach.
Lee EK; Zhang WJ; Zhang X; Adler PR; Lin S; Feingold BJ; Khwaja HA; Romeiko XX
Sci Total Environ; 2020 Apr; 714():136697. PubMed ID: 31982745
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
