Identification of spatiotemporal nutrient patterns in a coastal bay via an integrated k-means clustering and gravity model

Journal of Environmental Monitoring : JEM
Ni-Bin ChangZhemin Xuan

Abstract

This study presents an integrated k-means clustering and gravity model (IKCGM) for investigating the spatiotemporal patterns of nutrient and associated dissolved oxygen levels in Tampa Bay, Florida. By using a k-means clustering analysis to first partition the nutrient data into a user-specified number of subsets, it is possible to discover the spatiotemporal patterns of nutrient distribution in the bay and capture the inherent linkages of hydrodynamic and biogeochemical features. Such patterns may then be combined with a gravity model to link the nutrient source contribution from each coastal watershed to the generated clusters in the bay to aid in the source proportion analysis for environmental management. The clustering analysis was carried out based on 1 year (2008) water quality data composed of 55 sample stations throughout Tampa Bay collected by the Environmental Protection Commission of Hillsborough County. In addition, hydrological and river water quality data of the same year were acquired from the United States Geological Survey's National Water Information System to support the gravity modeling analysis. The results show that the k-means model with 8 clusters is the optimal choice, in which cluster 2 at Lower Tampa...Continue Reading

References

Nov 8, 2001·Environment International·J Bobbin, F Recknagel
Aug 16, 2008·Science·Robert J Diaz, Rutger Rosenberg

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Citations

Nov 4, 2020·Environmental Monitoring and Assessment·Ashwitha Krishnaraj, Paresh Chandra Deka

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