水资源与水工程学报

于海姣，温小虎，冯起，何志斌.基于支持向量机(SVM)的祁连山典型小流域日降水-径流模拟研究水资源与水工程学报[J].,2015,26(2):26-31

基于支持向量机(SVM)的祁连山典型小流域日降水-径流模拟研究

Simulation of daily rainfall-runoff in a small typical catchment of Qilian mountains based on support vector machine

DOI：10.11705/j.issn.1672-643X.2015.02.006

中文关键词: 小流域；日降水-径流模拟；日径流量；支持向量机祁连山

英文关键词:small catchment daily rainfall-runoff simulation daily runoff support vector machine Qilian mountains

基金项目:国家自然科学基金项目（41271524、31370466）；国家科技支撑项目（2012BAC08B05）

作者	单位
于海姣^1,2，温小虎¹，冯起¹，何志斌¹	（1.中国科学院寒区旱区环境与工程研究所，甘肃兰州 730000； 2.中国科学院大学，北京100049）

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中文摘要:

及时准确的日径流预测在流域水资源的合理规划、利用及管理中具有十分重要的作用。本文以支持向量机（SVM）模型为基础，以祁连山典型小流域-排露沟流域为研究区域，建立了流域日降水-径流模型，对流域未来1~7 d的日径流量进行了模拟预测。为检验SVM模型的有效性，模拟结果与人工神经网络（ANN）模型预测结果进行了对比。结果表明：SVM和ANN均表现出了很高的精度；但相比于传统的ANN模型，SVM模型的预测精度显著提高。表明SVM模型在半干旱山区小流域径流预测中有更好的适用性，可以用于流域中长期日径流预测，是资料有限的条件下中长期日径流预测的有效工具。

英文摘要:

To predict daily runoff timely and accurately plays an important role in the reasonable planning,utilization and management of water resources.This paper built daily rainfall-runoff model to predict daily runoff for seven days in Pailugou catchment in a typical catchment of Qilian mountains based on support vector machine(SVM).In order to test the validity of the developed model,it compared the results between SVM model and traditional artificial neural network(ANN) model in terms of different evaluation criteria during validation period.Results showed that both SVM and ANN presents very high precision and SVM model performed better than ANN model.The SVM model may be considered as an effective tool to establish a medium and long-term daily runoff forecast model in semiarid mountain regions under limited data condition.

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