Daily Discharge Forecast of Karst Underground River
详细信息
- 责任者：Wen Zhonghui (State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering ; Hohai University ; Nanjing 210098 ; China) ; Ren Huazhun
- 刊名：Journal of Jilin University
- 出版年：2011
- 卷期：41(2)
- 关键词：岩溶水 ; karst water ; 地下河 ; underground streams
- 页码：p.455-458,464
- 图表：3 illus., 1 table, 13 refs.
- 分类号：1701
- 出版序号：20120518
- issnNo：ISSN1671-5888
- isbnNo：CN22-1343/P
摘要
Considering the high nonlinearity of karst aquifer system and under the conditions of time-series on a small sample, the authors introduce the support vector regression method, which can be used to solve the small sample size and non-linear problem, use the partial least-squares regression to analyze the numerous factors impacting the daily discharge of underground river and extract the principal component as the input variables of support vector machine and use genetic algorithms to optimize model parameters. The results show that prediction accuracy of the model is significantly better than the BP neural network and multiple regression model.
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