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中文摘要：
需水预测是一个由城市人口、工业水平、社会经济水平共同作用的多因素、多层次的复杂非线性系统。其结果将直接影响受区域水资源承载力约束的产业结构、布局形态等决策。作为一种集中参数预报方法．支持向量机方法具有对未来样本的较好的泛化性能，对于这类资料缺乏、系统结构尚欠清晰的问题可以取得较好的模拟和预测结果。基于此，本文将支持向量机方法引入需水预测领域，建立了需水预测支持向量机模型。同时，本文将加速遗传算法和支持向量机方法耦合起来，构造了支持向量机模型参数的自适应优化算法。模型在珠海市的应用实例表明：与简单遗传算法比较．AGA的模型参数寻优效率更高；与BP神经网络模型相比，SVM模型较好地解决了小样本、经验性等问题，并取得了较高的预测精度。
英文摘要：
Water demand prediction is a complicated muhifactor, with the multi-level non-linear system influenced by urban population, industrial and social economic level. And water demand is the important reference basis in decision of regional industrial structure and arrangement form. As a centralized parameter forecast method, support vector machine may generalize the future sample performance. Application of this method may yield good simulation and prediction results to solve problems that lack of clear physical structure and enough data. So, this paper applied SVM method to build SVM water demand prediction model, and coupled SVM with AGA to optimize parameters of SVM model. The model was applied as a case in Zhuhai. The results show that compared with simple GA, AGA has a better efficiency in SVM model parameter optimization, and compared with BPNN model, SVM model can make a more precise forecasting by solving problems such as little sample, empiricism and etc.
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