%0 Journal Article
%T Estimation of Water Quality Parameters Using the Regression Model with Fuzzy K-Means Clustering
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%A A. Shareef, Muntadher
%A Toumi, Abdelmalek
%A Khenchaf, Ali
%< avec comité de lecture
%@ 2158-107X
%J International journal of advanced computer science and applications (IJACSA)
%I The Science and Information Organization
%V 5,6
%P xx
%8 2014-07-01
%D 2014
%R 10.14569/IJACSA.2014.050624
%K In situ data measurements
%K IKONOS data
%K water quality parameters
%K GLCM
%K empirical models
%K fuzzy K-means clustering
%Z Engineering Sciences [physics]/Electromagnetism
%Z Engineering Sciences [physics]/Signal and Image processing
%Z Computer Science [cs]/Signal and Image ProcessingJournal articles
%X 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.
%G English
%L hal-01062373
%U https://hal.science/hal-01062373
%~ UNIV-BREST
%~ INSTITUT-TELECOM
%~ ENSTA-BRETAGNE
%~ CNRS
%~ UNIV-UBS
%~ ENSTA-BRETAGNE-STIC
%~ ENIB
%~ LAB-STICC_ENIB
%~ LAB-STICC
%~ INSTITUTS-TELECOM