基于关联规则挖掘的径流长期预报模型研究  

Study of Long-term Runoff Forecast Model Based on Association Rules Mining

作　　者：王富强[1] 许士国[1] 

机构地区：[1]大连理工大学土木水利学院,辽宁大连116024

出　　处：《南水北调与水利科技》2007年第1期70-73,共4页South-to-North Water Transfers and Water Science & Technology

基　　金：国家自然科学基金重点项目(50139020)

摘　　要：关联规则是一种重要的数据挖掘技术。现结合水文长期预报的特殊性,将关联规则挖掘分析方法应用于径流长期预报中。根据预报目标初选出预报因子,构成长期预报事务数据集。然后将其进行离散化处理,对离散化后的数据集进行关联规则分析,挖掘出满足事先设定的最小支持度和最小置信度的强关联规则,解释规则并建立模型。以嫩江江桥站汛期径流长期预报为例,挖掘出满足要求的强关联规则,这些强关联规则中蕴含着北太平洋海温变化和江桥汛期径流的关系,说明了关联规则挖掘分析方法在径流长期预报中的可行性。Association rule is an important method of data mining techniques. Considering the characteristics of hydrology forecast, association rules method is applied to the long-term runoff forecast. The forecast factors are selected to constitute the long-term forecast database on the forecast object. The original data is discretized to find the strong association rules which accords with the minimum-support and minimum confidence. Long-term runoff forecast model is built based on the strong association rules. According to the practical example of Jiangqiao hydrologic station at Nenjiang River, the strong association rules reveal the relation between the sea surface temperature （SST） and the flood season runoff, and the association rules method is proved feasible in aided decision-making for long-term runoff forecast.

关 键 词：海温 关联规则 数据挖掘 长期预报 

分 类 号：P338[天文地球—水文科学]