Title
Integrated Data Fusion And Mining Techniques For Monitoring Total Organic Carbon Concentrations In A Lake
Abstract
Monitoring water quality on a near-real-time basis to address water resource management and public health concerns in coupled natural systems and the built environment is by no means an easy task. Total organic carbon (TOC) in surface waters is a known precursor of disinfection by-products in drinking water treatment such as total trihalomethanes (TTHMs), which are a suspected carcinogen and have been related to birth defects if water treatment plants cannot remove them. In this paper, an early warning system using integrated data fusion and mining (IDFM) techniques was proposed to estimate spatiotemporal distributions of TOC on a daily basis for monitoring water quality in a lake that serves as the source of a drinking water treatment plant. Landsat satellite images have high spatial resolution, but such application suffers from a long overpass interval of 16 days. On the other hand, coarse-resolution sensors with frequent revisit times, such as MODIS, are incapable of providing detailed water quality information because of low spatial resolution. This issue can be resolved by using data or sensor fusion techniques, such as IDFM, in which the high-spatial-resolution Landsat and the high-temporal-resolution MODIS images are fused and analysed by a suite of regression models to optimally produce synthetic images with both high spatial and temporal resolution. Analysis of the results using four statistical indices confirmed that the genetic programming model can accurately estimate the spatial and temporal variations of TOC concentrations in a small lake. The model entails a slight bias towards overestimating TOC, and it requires cloud-free input data for the lake. The IDFM efforts lead to the reconstruction of the spatiotemporal TOC distributions in a lake in support of healthy drinking water treatment. © 2014 © 2014 Taylor & Francis.
Publication Date
1-1-2014
Publication Title
International Journal of Remote Sensing
Volume
35
Issue
3
Number of Pages
1064-1093
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1080/01431161.2013.875632
Copyright Status
Unknown
Socpus ID
84893974700 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84893974700
STARS Citation
Chang, Ni Bin; Vannah, Benjamin W.; Yang, Y. Jeffrey; and Elovitz, Michael, "Integrated Data Fusion And Mining Techniques For Monitoring Total Organic Carbon Concentrations In A Lake" (2014). Scopus Export 2010-2014. 9632.
https://stars.library.ucf.edu/scopus2010/9632