Abstract
In order to understand the risk level of the shallow groundwater pollution in Huaibei, a method of the support vector machine (SVM) based on the uncertainty theory was proposed to evaluate the water quality. First, the principle of SVM was introduced, and a multi-class classifier of the SVM model was established based on it. Data from some water samples were used to train in the SVM model secondly. And then the model was used to predict the unknown samples, which were fit to the results calculated by fuzzy comprehensive evaluation (FCE). The results show that the SVM model of groundwater quality evaluation is reliable. It is suitable for the data processing based on finite number of training samples, with special technique to restrict overfitting. And method to establish the SVM model is simple.
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Acknowledgments
The authors gratefully acknowledge Mr. Liang of Nan jing University and Mr. Li for they meaningful comments on this manuscript and support.
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© 2011 Springer-Verlag Berlin Heidelberg
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Lu, H., Yuan, B. (2011). Shallow Groundwater Quality Evaluation in Huaibei Based on the Uncertainty Theory. In: Wu, D., Zhou, Y. (eds) Modeling Risk Management for Resources and Environment in China. Computational Risk Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18387-4_10
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DOI: https://doi.org/10.1007/978-3-642-18387-4_10
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