Article ID: | iaor20062823 |
Country: | Canada |
Volume: | 4 |
Issue: | 2 |
Start Page Number: | 65 |
End Page Number: | 74 |
Publication Date: | Dec 2004 |
Journal: | Journal of Environmental Informatics |
Authors: | Panda S.S., Garg V., Chaubey I. |
Keywords: | space, neural networks, biology, geography & environment |
Lake water quality monitoring using traditional water sampling and laboratory analyses is very expensive and time consuming. Application of neural networks to predict water quality using satellite imagery data has a potential to make the water quality determination process cost-effective, quick, and feasible. This paper includes an indirect method of determining the concentrations of chlorophyll-a (chl-a) and suspended matter (SM), two optically active parameters of lake water quality. Radial basis function neural (RBFN) network models are developed to predict the chl-a and SM concentrations in the lake. The low cost commercially available Landsat-TM imagery spectral information was used as the input with chl-a or SM concentrations as output. The model is trained and validated with data from the years 2001, 2002, 2003, and 2004. The model testing resulted in a coefficient of determination (R2) of 0.55, and 0.90, respectively, for actual and predicted chl-a and SM concentrations. The root mean square error (RMSE), standard error of prediction (SEP), and average testing accuracy indicated the merit of the developed models.