Estimation of Water Quality Parameters Using the Regression Model with Fuzzy K-Means Clustering
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2014, Vol 5, Issue 6
Abstract
The traditional methods in remote sensing used for monitoring and estimating pollutants are generally relied on the spectral response or scattering reflected from water. In this work, a new method has been proposed to find contaminants and determine the Water Quality Parameters (WQPs) based on theories of the texture analysis. Empirical statistical models have been developed to estimate and classify contaminants in the water. Gray Level Co-occurrence Matrix (GLCM) is used to estimate six texture parameters: contrast, correlation, energy, homogeneity, entropy and variance. These parameters are used to estimate the regression model with three WQPs. Finally, the fuzzy K-means clustering was used to generalize the water quality estimation on all segmented image. Using the in situ measurements and IKONOS data, the obtained results show that texture parameters and high resolution remote sensing able to monitor and predicate the distribution of WQPs in large rivers.
Authors and Affiliations
Muntadher SHAREEF, Abdelmalek TOUMI, Ali KHENCHAF
Keywords
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- EP ID EP142136
- DOI 10.14569/IJACSA.2014.050624
- Views 98
- Downloads 0
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