Abstract:Generally, the traditional method to evaluate the water quality using regression support vector machine is not an universal way. The learning efficiency and the accuracy will be significantly influenced due to the increase of the number of the index. For three different water bodies (surface water, groundwater and eutrophic water body), the present study sets the proper reference values and normalized transformation forms for all the indexes of them. The differences among all the indexes will be decreased for the same quality level water after the normal transformation, and the different indexes represented with normalized values can be equivalent to a certain value. Therefore, it is possible to build the water quality evaluation model using regression support vector machines (SVR) with any combination of m(2£m£72) normalized indexes. The practicality of the model was verified by samples from HeQiao surface water, HeiLongDong spring groundwater and eutrophication of SanZi reservoir. The evaluation gets the similar results with the evaluations using BP neural network , fuzzy sets and attribute recognition evaluationary methods.
李祚泳, 张正健. 基于回归支持向量机的指标规范值的水质评价模型[J]. 中国环境科学, 2013, 33(8): 1502-1508.
LI Zuo-Yong, ZHANG Zheng-Jian. Model of water quality evaluation with normalized indexes values based on regression support vector machines. CHINA ENVIRONMENTAL SCIENCECE, 2013, 33(8): 1502-1508.