This paper establishes the retrieving models of water quality parameters by remote sensing based on support vector machine （SVM）

and proposes a self-adaptive optimization algorithm for the selection of SVM model parameters using genetic algorithm （GA）. Using high resolution multispectral SPOT-5 data and in situ measurements

we construct univariate and multivariate empirical models for retrieving water quality parameters of Weihe River in Shaanxi province. The capability of the proposed GA-SVM method is obviously better than the neuron networks and the traditional statistical regression methods even for limited samples. And the results of multivariate models are always better than that of univariate models for these methods. Since SVM has the ability of non-linear mapping

fitting for small samples

and the model parameters are selected automatically by GA

GA-SVM method shows distinct superiority in solving our problems. By introducing the new method of machine learning and intelligent computing method for global optimization

GA-SVM provides a new approach for water quality monitoring by remote sensing

and can obtain better results for Weihe River in Shaanxi.