196 related articles for article (PubMed ID: 36399296)
1. 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]
2. Lake water-level fluctuation forecasting using machine learning models: a systematic review.
Zhu S; Lu H; Ptak M; Dai J; Ji Q
Environ Sci Pollut Res Int; 2020 Dec; 27(36):44807-44819. PubMed ID: 32978734
[TBL] [Abstract][Full Text] [Related]
3. Forecasting municipal solid waste generation using artificial intelligence modelling approaches.
Abbasi M; El Hanandeh A
Waste Manag; 2016 Oct; 56():13-22. PubMed ID: 27297046
[TBL] [Abstract][Full Text] [Related]
4. Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review.
Ling JYX; Chan YJ; Chen JW; Chong DJS; Tan ALL; Arumugasamy SK; Lau PL
Environ Sci Pollut Res Int; 2024 Mar; 31(13):19085-19104. PubMed ID: 38376778
[TBL] [Abstract][Full Text] [Related]
5. Artificial intelligence and machine learning approaches in composting process: A review.
Aydın Temel F; Cagcag Yolcu O; Turan NG
Bioresour Technol; 2023 Feb; 370():128539. PubMed ID: 36608858
[TBL] [Abstract][Full Text] [Related]
6. Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing.
Javadi F; Qaderi K; Ahmadi MM; Rahimpour M; Madadi MR; Mahdavi-Meymand A
Sci Rep; 2022 Nov; 12(1):19390. PubMed ID: 36371476
[TBL] [Abstract][Full Text] [Related]
7. Short-term solar radiation forecasting using machine learning models under different sky conditions: evaluations and comparisons.
Belmahdi B; Bouardi AE
Environ Sci Pollut Res Int; 2024 Jan; 31(1):966-981. PubMed ID: 38030838
[TBL] [Abstract][Full Text] [Related]
8. Seminal quality prediction using data mining methods.
Sahoo AJ; Kumar Y
Technol Health Care; 2014; 22(4):531-45. PubMed ID: 24898862
[TBL] [Abstract][Full Text] [Related]
9. Prediction of the five-day biochemical oxygen demand and chemical oxygen demand in natural streams using machine learning methods.
Najafzadeh M; Ghaemi A
Environ Monit Assess; 2019 May; 191(6):380. PubMed ID: 31104155
[TBL] [Abstract][Full Text] [Related]
10. Groundwater level forecasting with machine learning models: A review.
Boo KBW; El-Shafie A; Othman F; Khan MMH; Birima AH; Ahmed AN
Water Res; 2024 Mar; 252():121249. PubMed ID: 38330715
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Performance assessment of a solar powered hydrogen production system and its ANFIS model.
Senthilraja S; Gangadevi R; Köten H; Marimuthu R
Heliyon; 2020 Oct; 6(10):e05271. PubMed ID: 33102870
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms.
Yazdani A; Shanbehzadeh M; Kazemi-Arpanahi H
BMC Med Inform Decis Mak; 2023 Oct; 23(1):229. PubMed ID: 37858200
[TBL] [Abstract][Full Text] [Related]
16. Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine.
Li Y; Jiang P; She Q; Lin G
Environ Pollut; 2018 Oct; 241():1115-1127. PubMed ID: 30029320
[TBL] [Abstract][Full Text] [Related]
17. Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction.
Claywell R; Nadai L; Felde I; Ardabili S; Mosavi A
Entropy (Basel); 2020 Oct; 22(11):. PubMed ID: 33286960
[TBL] [Abstract][Full Text] [Related]
18. Comparative optimization of global solar radiation forecasting using machine learning and time series models.
Belmahdi B; Louzazni M; El Bouardi A
Environ Sci Pollut Res Int; 2022 Feb; 29(10):14871-14888. PubMed ID: 34625894
[TBL] [Abstract][Full Text] [Related]
19. Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models.
Ke B; Nguyen H; Bui XN; Bui HB; Choi Y; Zhou J; Moayedi H; Costache R; Nguyen-Trang T
Chemosphere; 2021 Aug; 276():130204. PubMed ID: 34088091
[TBL] [Abstract][Full Text] [Related]
20. Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets.
Wu Z; Zhu M; Kang Y; Leung EL; Lei T; Shen C; Jiang D; Wang Z; Cao D; Hou T
Brief Bioinform; 2021 Jul; 22(4):. PubMed ID: 33313673
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
