• DocumentCode
    612921
  • Title

    Monitoring nutrient concentrations in Tampa Bay with MODIS images and machine learning models

  • Author

    Ni-Bin Chang ; Zhemin Xuan

  • Author_Institution
    Dept. of Civil, Environ., & Constr. Eng., Univ. of Central Florida, Orlando, FL, USA
  • fYear
    2013
  • fDate
    10-12 April 2013
  • Firstpage
    702
  • Lastpage
    707
  • Abstract
    This paper explores the spatiotemporal nutrient patterns in Tampa Bay, Florida with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) images and Genetic Programming (GP) models that are designed to link Total Phosphorus (TP) levels and remote sensing reflectance bands in aquatic environments. In-situ data were drawn from a local database to support the calibration and validation of the GP model. The GP models show the effective capacity to demonstrating the snapshots of spatiotemporal distributions of TP across the Bay, which helps to delineate the short-term seasonality effect and the global trend of TP in the coastal bay. The model output can provide informative reference for the establishment of contingency plans in treating nutrients-rich runoff.
  • Keywords
    environmental science computing; genetic algorithms; geophysical image processing; learning (artificial intelligence); phosphorus; remote sensing; water treatment; GP model; MODIS image; TP; Tampa Bay; aquatic environment; coastal bay; genetic programming; machine learning model; moderate resolution imaging spectroradiometer; nutrient concentration monitoring; remote sensing reflectance band; short-term seasonality effect; total phosphorus; Biomedical monitoring; Cities and towns; Computational modeling; Data mining; Genetics; Monitoring; Reflectivity; MODIS; Remote sensing; coastal bay; genetic programming; nutrient monitoring; wastewater treatment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networking, Sensing and Control (ICNSC), 2013 10th IEEE International Conference on
  • Conference_Location
    Evry
  • Print_ISBN
    978-1-4673-5198-0
  • Electronic_ISBN
    978-1-4673-5199-7
  • Type

    conf

  • DOI
    10.1109/ICNSC.2013.6548824
  • Filename
    6548824