关键词: 地下河, 小样本, 偏最小二乘, 遗传算法, 支持向量回归

Abstract:

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. PLS-Genetic-Support vector regression model is established for the daily flow forecast of underground river and is use to forecast the daily flow of the typical karst underground river area in Houzhai, the mean square error and average relative error of PLS-Genetic-Support vector regression model is 0.25% and 6.89% in simulation period, and it is 0.65% and 6.03% in forecast period, the mean square error and average relative error of artificial neural network model is 0.24% and 7.30% in simulation period, and it is 0.84% and 7.39% in forecast period, and the mean square error and average relative error of multiple regression model is 0.28% and 9.3% in simulation period, and it is 1.10% and 10.54% in forecast period. The results show that prediction accuracy of the model is significantly better than the BP neural network and multiple regression model.

Key words: underground river, small sample, partial least-squares, genetic algorithms, support vector regression