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

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@InProceedings{Chang:2013:ieeeICNSCtampa,

• author = "Ni-Bin Chang and Zhemin Xuan",
• booktitle = "10th IEEE International Conference on Networking, Sensing and Control (ICNSC 2013)",
• title = "Monitoring nutrient concentrations in {Tampa Bay} with MODIS images and machine learning models",
• year = "2013",
• month = apr,
• pages = "702--707",
• keywords = "genetic algorithms, genetic programming, environmental science computing, geophysical image processing, learning (artificial intelligence), phosphorus, remote sensing, water treatment, GP model, MODIS image, TP, Tampa Bay, aquatic environment, coastal bay, machine learning model, moderate resolution imaging spectroradiometer, nutrient concentration monitoring, remote sensing reflectance band, short-term seasonality effect, total phosphorus, MODIS, Remote sensing, coastal bay, nutrient monitoring, wastewater treatment",
• DOI = "doi:10.1109/ICNSC.2013.6548824",
• 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 spatio-temporal 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.",
• notes = "Also known as \cite{6548824}",

}

Genetic Programming entries for Ni-Bin Chang Zhemin Xuan

Citations