摘要
地下水位的动态变化是一个复杂的非线性过程,地下水位与其影响因素之间存在着复杂的非线性关系,将支持向量机方法应用于地下水位预测,并提出相应的模型.实例分析结果表明,与基于GM(1,1)的预测模型相比,地下水位预测的支持向量机模型科学可行,预测精度高,对地下水位预测的问题具有较好的适用性.
Considering that dynamic fluctuation of underground water level is a complicated nonlinear process and there are complex nonlinear relationships between underground water level and the impact factors,Support Vector Machine（SVM） is applied to forecast underground water level and the corresponding model is proposed.The case study shows that SVM model is more feasible than the GM（1,1） model and the forecast precision of SVM model is high,thus it is applicable for underground water level prediction.
出处
《华北水利水电学院学报》
2011年第2期11-14,共4页
North China Institute of Water Conservancy and Hydroelectric Power
基金
郑州市科技计划项目(0910SGYG21201-6)
关键词
统计学习
支持向量机
地下水位
statistical learning
Support Vector Machine（SVM）
underground water level
