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把水资源可持续利用评价问题看成是一个分类问题,利用支持向量机良好的鲁棒性和分类精确性进行评价,并用遗传算法优化了SVM的参数,使其分类精确度更高.对黑龙江省十三个地区进行了实例应用,与人工神经网络和GD-IIM法的结果进行了比较,结果表明,支持向量机模型简单、通用、精度高,可在水资源可持续利用实际评价中推广应用.
The evaluation of sustainable use of water resources is regarded as a classification problem, which is evaluated by the good robustness and classification accuracy of support vector machines, and the genetic algorithm is used to optimize the parameters of SVM so that the classification accuracy is higher. Thirteen regions in Jiangsu Province were used as examples to compare with the results of artificial neural network and GD-IIM method. The results show that SVM model is simple, universal and accurate, which can be applied in practical evaluation of sustainable utilization of water resources application.