摘要
晋祠泉域是一个复杂的大型岩溶泉系统,地下水位的动态与降雨量、岩溶水开采、关井压采及煤炭资源整合等因素密切相关。从动态观测信息入手,借助支持向量机自学习理论,建立了晋祠泉域高维空间输入、输出信息的非线性岩溶水位动态预测模型,较好地表达或预测了岩溶水位。
Jinci spring area is a complex large-scale karst spring system, the change of groundwater level is closely related to the factors of rainfall, karst water exploitation, coal mining and coal resource utilization. Based on the observation data of dynamic＇ groundwater level, a prediction model of high-dimensional spatial nonlinear karst groundwater level for Jinci spring area is established by using the self-learning theory of Support Vector Machine. The karst water level is well predicted by the model.
作者
李扭串
LI Niuchuan(Shanxi Water Resources and Hydropower Survey and Design Institute, Taiyuan 030024, Shanxi, Chin)
出处
《水力发电》
北大核心
2017年第8期31-33,43,共4页
Water Power
关键词
岩溶水位
预测模型
高维空间
非线性
支持向量机
karst water level
prediction model
high-dimensional space
nonlinear
support vector machine
