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Journal Abstract Search

318 related items for PubMed ID: 25241206

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  • 5. Early warning of algal blooms based on the optimization support vector machine regression in a typical tributary bay of the Three Gorges Reservoir, China.
    Xia J, Zeng J.
    Environ Geochem Health; 2022 Dec; 44(12):4719-4733. PubMed ID: 35267125
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  • 6. Trophic state modeling for shallow freshwater reservoir: a new approach.
    Markad AT, Landge AT, Nayak BB, Inamdar AB, Mishra AK.
    Environ Monit Assess; 2019 Aug 22; 191(9):586. PubMed ID: 31440835
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  • 7. 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 22; 10(3):e0119082. PubMed ID: 25768650
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  • 8. Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm.
    Li S, Song K, Wang S, Liu G, Wen Z, Shang Y, Lyu L, Chen F, Xu S, Tao H, Du Y, Fang C, Mu G.
    Sci Total Environ; 2021 Jul 15; 778():146271. PubMed ID: 33721636
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  • 14. A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs.
    Yu W, Wang X, Jiang X, Zhao R, Zhao S.
    Environ Sci Pollut Res Int; 2024 Jan 15; 31(1):262-279. PubMed ID: 38015396
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  • 15. ANN application for prediction of atmospheric nitrogen deposition to aquatic ecosystems.
    Palani S, Tkalich P, Balasubramanian R, Palanichamy J.
    Mar Pollut Bull; 2011 Jun 15; 62(6):1198-206. PubMed ID: 21481425
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  • 17. Linking water environmental factors and the local watershed landscape to the chlorophyll a concentration in reservoir bays.
    Li N, Wang J, Yin W, Jia H, Xu J, Hao R, Zhong Z, Shi Z.
    Sci Total Environ; 2021 Mar 01; 758():143617. PubMed ID: 33213921
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  • 18. Chlorophyll-a prediction in tropical reservoirs as a function of hydroclimatic variability and water quality.
    Guimarães BMDM, Neto IEL.
    Environ Sci Pollut Res Int; 2023 Aug 01; 30(39):91028-91045. PubMed ID: 37468780
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  • 19. Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.
    Lee B, Im JK, Han JW, Kang T, Kim W, Kim M, Lee S.
    Environ Sci Pollut Res Int; 2024 Oct 01; 31(48):58505-58526. PubMed ID: 39316212
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  • 20. Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: a probability bounds approach.
    Betrie GD, Sadiq R, Morin KA, Tesfamariam S.
    Sci Total Environ; 2014 Aug 15; 490():182-90. PubMed ID: 24852616
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