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Artificial Neural Networks Application in Lake Water Quality Estimation Using Satellite Imagery

S. S. Panda, V. Garg, I. Chaubey*

Biological and Agricultural Engineering Department, 203 Engineering Hall, University of Arkansas, Fayetteville, AR 72701

*Corresponding author. Email:


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.

Keywords: Chlorophyll, image processing, RBFN, remote sensing, suspended matter

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