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.