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Journal Abstract Search

298 related items for PubMed ID: 17078569

  • 1. Prediction of the concentration of chlorophyll-a for Liuhai urban lakes in Beijing City.
    Zeng Y, Yang ZF, Liu JL.
    J Environ Sci (China); 2006; 18(4):827-31. PubMed ID: 17078569
    [Abstract] [Full Text] [Related]

  • 2. Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-a case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake.
    Xu M, Zeng GM, Xu XY, Huang GH, Sun W, Jiang XY.
    J Environ Sci (China); 2005; 17(6):946-52. PubMed ID: 16465884
    [Abstract] [Full Text] [Related]

  • 3. [Establishment of the predictive model of source eutrophication using artificial neural network].
    Yang S, Zhang H, Ba Y, Cheng X.
    Wei Sheng Yan Jiu; 2008 Sep; 37(5):543-5. PubMed ID: 19069648
    [Abstract] [Full Text] [Related]

  • 4. Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.
    Park Y, Cho KH, Park J, Cha SM, Kim JH.
    Sci Total Environ; 2015 Jan 01; 502():31-41. PubMed ID: 25241206
    [Abstract] [Full Text] [Related]

  • 5. Comparison of artificial neural network and multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes.
    Arulsudar N, Subramanian N, Muthy RS.
    J Pharm Pharm Sci; 2005 Aug 05; 8(2):243-58. PubMed ID: 16124936
    [Abstract] [Full Text] [Related]

  • 6. Application of artificial neural networks to assess pesticide contamination in shallow groundwater.
    Sahoo GB, Ray C, Mehnert E, Keefer DA.
    Sci Total Environ; 2006 Aug 15; 367(1):234-51. PubMed ID: 16460784
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  • 10. Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao Reservoir, China.
    Liu Y, Xi DG, Li ZL.
    PLoS One; 2015 Aug 15; 10(3):e0119082. PubMed ID: 25768650
    [Abstract] [Full Text] [Related]

  • 11. An ANN application for water quality forecasting.
    Palani S, Liong SY, Tkalich P.
    Mar Pollut Bull; 2008 Sep 15; 56(9):1586-97. PubMed ID: 18635240
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  • 12. Multiplatform optical monitoring of eutrophication in temporally and spatially variable lakes.
    Vos RJ, Hakvoort JH, Jordans RW, Ibelings BW.
    Sci Total Environ; 2003 Aug 01; 312(1-3):221-43. PubMed ID: 12873412
    [Abstract] [Full Text] [Related]

  • 13. Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction.
    Noori R, Karbassi A, Salman Sabahi M.
    J Environ Manage; 2010 Aug 01; 91(3):767-71. PubMed ID: 19913989
    [Abstract] [Full Text] [Related]

  • 14. Modeling biological oxygen demand of the Melen River in Turkey using an artificial neural network technique.
    Dogan E, Sengorur B, Koklu R.
    J Environ Manage; 2009 Feb 01; 90(2):1229-35. PubMed ID: 18691805
    [Abstract] [Full Text] [Related]

  • 15. Linear and nonlinear modeling approaches for urban air quality prediction.
    Singh KP, Gupta S, Kumar A, Shukla SP.
    Sci Total Environ; 2012 Jun 01; 426():244-55. PubMed ID: 22542239
    [Abstract] [Full Text] [Related]

  • 16. The use of artificial neural network (ANN) for modeling of COD removal from antibiotic aqueous solution by the Fenton process.
    Elmolla ES, Chaudhuri M, Eltoukhy MM.
    J Hazard Mater; 2010 Jul 15; 179(1-3):127-34. PubMed ID: 20307930
    [Abstract] [Full Text] [Related]

  • 17. Long term changes in the eutrophication process in a shallow Mediterranean lake ecosystem of W. Greece: response after the reduction of external load.
    Kagalou I, Papastergiadou E, Leonardos I.
    J Environ Manage; 2008 May 15; 87(3):497-506. PubMed ID: 17383796
    [Abstract] [Full Text] [Related]

  • 18. Predicting conductance due to upconing using neural networks.
    Coppola EA, McLane CF, Poulton MM, Szidarovszky F, Magelky RD.
    Ground Water; 2005 May 15; 43(6):827-36. PubMed ID: 16324004
    [Abstract] [Full Text] [Related]

  • 19. Estimating monthly total nitrogen concentration in streams by using artificial neural network.
    He B, Oki T, Sun F, Komori D, Kanae S, Wang Y, Kim H, Yamazaki D.
    J Environ Manage; 2011 Jan 15; 92(1):172-7. PubMed ID: 20870340
    [Abstract] [Full Text] [Related]

  • 20. Artificial neural network model for earthquake prediction with radon monitoring.
    Külahci F, Inceöz M, Doğru M, Aksoy E, Baykara O.
    Appl Radiat Isot; 2009 Jan 15; 67(1):212-9. PubMed ID: 18789709
    [Abstract] [Full Text] [Related]

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