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Journal Abstract Search

503 related items for PubMed ID: 32078849

  • 1. Hybrid decision tree-based machine learning models for short-term water quality prediction.
    Lu H, Ma X.
    Chemosphere; 2020 Jun; 249():126169. PubMed ID: 32078849
    [Abstract] [Full Text] [Related]

  • 2. Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed.
    Anmala J, Turuganti V.
    Water Environ Res; 2021 Nov; 93(11):2360-2373. PubMed ID: 34528328
    [Abstract] [Full Text] [Related]

  • 3. Dynamic real-time forecasting technique for reclaimed water volumes in urban river environmental management.
    Zhang L, Wang C, Hu W, Wang X, Wang H, Sun X, Ren W, Feng Y.
    Environ Res; 2024 May 01; 248():118267. PubMed ID: 38244969
    [Abstract] [Full Text] [Related]

  • 4. Prediction of 5-day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms.
    Nafsin N, Li J.
    Water Environ Res; 2022 May 01; 94(5):e10718. PubMed ID: 35502725
    [Abstract] [Full Text] [Related]

  • 5. Determination of biochemical oxygen demand and dissolved oxygen for semi-arid river environment: application of soft computing models.
    Tao H, Bobaker AM, Ramal MM, Yaseen ZM, Hossain MS, Shahid S.
    Environ Sci Pollut Res Int; 2019 Jan 01; 26(1):923-937. PubMed ID: 30421367
    [Abstract] [Full Text] [Related]

  • 6. Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters.
    Fijani E, Barzegar R, Deo R, Tziritis E, Skordas K.
    Sci Total Environ; 2019 Jan 15; 648():839-853. PubMed ID: 30138884
    [Abstract] [Full Text] [Related]

  • 7. Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.
    Heddam S, Kisi O.
    Environ Sci Pollut Res Int; 2017 Jul 15; 24(20):16702-16724. PubMed ID: 28560629
    [Abstract] [Full Text] [Related]

  • 8. A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.
    Jiao J, Ma Q, Liu F, Zhao L, Huang S.
    Environ Geochem Health; 2024 Mar 14; 46(4):127. PubMed ID: 38483668
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  • 11. Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm.
    Feng M, Duan Y, Wang X, Zhang J, Ma L.
    Sci Rep; 2023 Oct 27; 13(1):18447. PubMed ID: 37891187
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  • 13. Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.
    Yang J, Zheng Y, Zhang W, Zhou Y, Zhang Y.
    J Environ Manage; 2024 Jul 27; 364():121386. PubMed ID: 38865920
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  • 15. 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 19; 191(6):380. PubMed ID: 31104155
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  • 16. Predictive modeling of blood pressure during hemodialysis: a comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method.
    Huang JC, Tsai YC, Wu PY, Lien YH, Chien CY, Kuo CF, Hung JF, Chen SC, Kuo CH.
    Comput Methods Programs Biomed; 2020 Oct 19; 195():105536. PubMed ID: 32485511
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  • 19. Prediction of groundwater quality using efficient machine learning technique.
    Singha S, Pasupuleti S, Singha SS, Singh R, Kumar S.
    Chemosphere; 2021 Aug 19; 276():130265. PubMed ID: 34088106
    [Abstract] [Full Text] [Related]

  • 20. Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria.
    Ogwueleka TC.
    Environ Monit Assess; 2015 Mar 19; 187(3):137. PubMed ID: 25707603
    [Abstract] [Full Text] [Related]

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