并联组合建模在径流预测中的应用  

Application of parallel combination modeling in runoff prediction

作　　者：黎育红[1] 姜福厚[1] 刘任改[1] 

机构地区：[1]华中科技大学水电与数字化工程学院,湖北武汉430074

出　　处：《水资源与水工程学报》2013年第1期45-49,共5页Journal of Water Resources and Water Engineering

基　　金：水利部公益性行业科研专项(201001080);华中科技大学科学研究基金;中央高校基本科研业务费资助(HUST2011QN067)联合资助

摘　　要：针对径流预报的具体特征和相关问题,本文首先建立多元时变灰色预测模型,在分析多元时变灰色预测模型、非时变的免疫神经网络模型、最小二乘支持向量机模型在径流预报中应用的优势和不足的基础上,讨论并联组合预测建模的实用意义,并基于提高样本数据的精度将三者进行并联组合集成建模,充分发挥多种模型各自优点且相互补充。最后以新疆伊犁河雅马渡水文站的年径流预测为例,对该站年径流量进行并联组合预测建模,通过与三个单项模型的预测结果的比较分析,证实了本文所提出的组合预测的合理性、普适性和可靠性。Aimed at the specific characteristics of runoff forecast and related issues,this paper first established a diversity changeable gray prediction model.Based on the analysis of the advantages and shorts of application of the diversity changeable gray prediction model,non-time-varying immune neural network model,least squares support vector machine model in runoff forecast,it discussed the practical significance of predictive modeling of parallel combination,and integrated the three model into parallel combination model so as to improve the precision of the sample data and full played their respective advantages and complement each other.Last,taking the annual runoff forecast of Xinjiang Yili Yamadu hydrological station for example,the paper set up the parallel combination model for annual runoff of the station,and confirmed the rationality,universality and reliability of the combination forecast model by comparative analysis of the predicted results of three individual models.

关 键 词：径流预报 信息熵 并联组合建模 GM(1 N) IEA-BP LS-SVM 

分 类 号：P333.1[天文地球—水文科学]