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中文摘要：
在阐述支持向量机的基本原理、二值分类和多值分类技术及各自特性的基础上，结合洪水灾情综合评价中受自然环境、社会经济等诸多因素的影响且实测样本资料较少的特点，以及目前已有评价模型不能或难以解决的小样本、“过学习”、局部最小等实际难题，提出了基于支持向量机的洪水灾情综合评价模型，并应用实例进行了验证。研究结果表明，此模型和传统的灾情评估法、人工神经网络评价模型一样有效合理，并且模型运算时间比人工神经网络模型要短。因此，不仅为洪水灾情综合评估提供了一种新的模型，而且由于支持向量机遵循统计学习理论中结构风险最小化的原理，具有解决有限样本、非线性及高维识别中的优势，必将比其他传统的评价模型得到更广泛的应用和发展。
英文摘要：
.Based on the principle of support vector machine （SVM） and two-class or multi-class classifier,a model of evaluating flood disaster loss was established in consideration of the character of many factors which are related to the evaluation and the small samples in practice, and some problems which are difficult or impossible to be resolved in present model, such as over-fitting, the local minimization and so on. The model was tested in a case which represented real flood disasters happened in some areas in China from 1989 to 1990. It is proved that the model is as useful as the model of artificial neural network （ANN） and other traditional methods,the time for calculating this model is much less than normal models. So the model established in this paper will be much more applicable in the future in the evaluation of flood disaster loss because of minimization of structure risks and its advantage in solving limited sample, non-liner and high dimensions recognization.
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