These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

192 related articles for article (PubMed ID: 36621510)

  • 1. Self-optimization of training dataset improves forecasting of cyanobacterial bloom by machine learning.
    Kim J; Jung W; An J; Oh HJ; Park J
    Sci Total Environ; 2023 Mar; 866():161398. PubMed ID: 36621510
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs.
    Schaeffer BA; Reynolds N; Ferriby H; Salls W; Smith D; Johnston JM; Myer M
    J Environ Manage; 2024 Jan; 349():119518. PubMed ID: 37944321
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Sub-monthly time scale forecasting of harmful algal blooms intensity in Lake Erie using remote sensing and machine learning.
    Gupta A; Hantush MM; Govindaraju RS
    Sci Total Environ; 2023 Nov; 900():165781. PubMed ID: 37499836
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach.
    Kim T; Shin J; Lee D; Kim Y; Na E; Park JH; Lim C; Cha Y
    Water Res; 2022 May; 215():118289. PubMed ID: 35303563
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method.
    Kim JH; Shin JK; Lee H; Lee DH; Kang JH; Cho KH; Lee YG; Chon K; Baek SS; Park Y
    Water Res; 2021 Dec; 207():117821. PubMed ID: 34781184
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A novel single-parameter approach for forecasting algal blooms.
    Xiao X; He J; Huang H; Miller TR; Christakos G; Reichwaldt ES; Ghadouani A; Lin S; Xu X; Shi J
    Water Res; 2017 Jan; 108():222-231. PubMed ID: 27847147
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Spatial and temporal characterization of cyanobacteria blooms in the Mississippi Sound and their relationship to the Bonnet Carré Spillway openings.
    Soto Ramos IM; Crooke B; Seegers B; Cetinić I; Cambazoglu MK; Armstrong B
    Harmful Algae; 2023 Aug; 127():102472. PubMed ID: 37544672
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea.
    Yi HS; Park S; An KG; Kwak KC
    Int J Environ Res Public Health; 2018 Sep; 15(10):. PubMed ID: 30248912
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes.
    Rousso BZ; Bertone E; Stewart R; Hamilton DP
    Water Res; 2020 Sep; 182():115959. PubMed ID: 32531494
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Cyanophage technology in removal of cyanobacteria mediated harmful algal blooms: A novel and eco-friendly method.
    Bhatt P; Engel BA; Reuhs M; Simsek H
    Chemosphere; 2023 Feb; 315():137769. PubMed ID: 36623591
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing.
    Urquhart EA; Schaeffer BA; Stumpf RP; Loftin KA; Werdell PJ
    Harmful Algae; 2017 Jul; 67():144-152. PubMed ID: 28755717
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data.
    Mishra S; Stumpf RP; Schaeffer B; Werdell PJ; Loftin KA; Meredith A
    Sci Total Environ; 2021 Jun; 774():145462. PubMed ID: 33609824
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Environmental drivers behind the exceptional increase in cyanobacterial blooms in Okavango Delta, Botswana.
    Veerman J; Mishra DR; Kumar A; Karidozo M
    Harmful Algae; 2024 Aug; 137():102677. PubMed ID: 39003028
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Using convolutional neural network for predicting cyanobacteria concentrations in river water.
    Pyo J; Park LJ; Pachepsky Y; Baek SS; Kim K; Cho KH
    Water Res; 2020 Nov; 186():116349. PubMed ID: 32882452
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Recent advances in algal bloom detection and prediction technology using machine learning.
    Park J; Patel K; Lee WH
    Sci Total Environ; 2024 Aug; 938():173546. PubMed ID: 38810749
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Measurement of Cyanobacterial Bloom Magnitude using Satellite Remote Sensing.
    Mishra S; Stumpf RP; Schaeffer BA; Werdell PJ; Loftin KA; Meredith A
    Sci Rep; 2019 Dec; 9(1):18310. PubMed ID: 31797884
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Science meets policy: A framework for determining impairment designation criteria for large waterbodies affected by cyanobacterial harmful algal blooms.
    Davis TW; Stumpf R; Bullerjahn GS; McKay RML; Chaffin JD; Bridgeman TB; Winslow C
    Harmful Algae; 2019 Jan; 81():59-64. PubMed ID: 30638499
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A machine learning approach for early warning of cyanobacterial bloom outbreaks in a freshwater reservoir.
    Park Y; Lee HK; Shin JK; Chon K; Kim S; Cho KH; Kim JH; Baek SS
    J Environ Manage; 2021 Jun; 288():112415. PubMed ID: 33774562
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Risks for cyanobacterial harmful algal blooms due to land management and climate interactions.
    Weber SJ; Mishra DR; Wilde SB; Kramer E
    Sci Total Environ; 2020 Feb; 703():134608. PubMed ID: 31757537
    [TBL] [Abstract][Full Text] [Related]  

  • 20. One-Week-Ahead Prediction of Cyanobacterial Harmful Algal Blooms in Iowa Lakes.
    Villanueva P; Yang J; Radmer L; Liang X; Leung T; Ikuma K; Swanner ED; Howe A; Lee J
    Environ Sci Technol; 2023 Dec; 57(49):20636-20646. PubMed ID: 38011382
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.