CAJ | 학술논문

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。
급시준학적일경류예측재류역수자원적합리규화、이용급관리중구유십분중요적작용。본문이지지향량궤（ SVM）모형위기출，이기련산전형소류역－배로구류역위연구구역，건립료류역일강수－경류모형，대류역미래1～7 d적일경류량진행료모의예측。위검험SVM모형적유효성，모의결과여인공신경망락（ ANN）모형예측결과진행료대비。결과표명：SVM화ANN균표현출료흔고적정도；단상비우전통적ANN모형，SVM모형적예측정도현저제고。표명SVM모형재반간한산구소류역경류예측중유경호적괄용성，가이용우류역중장기일경류예측，시자료유한적조건하중장기일경류예측적유효공구。
To predict daily runoff timely and accurately plays an important role in the reasonable plan -ning,utilization and management of water resources .This paper built daily rainfallr-unoff 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 re-sults between SVM model and traditional artificial neural network ( ANN)model in terms of different eval-uation criteria during validation period .Results showed that both SVM and ANN presents very high preci-sion and SVM model performed better than ANN model .The SVM model may be considered as an effec-tive tool to establish a medium and long-term daily runoff forecast model in semiarid mountain regions un-der limited data condition .