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
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;
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
DOI :
10.1109/ICNSC.2013.6548824