基于GA-SVR的中长期径流预报  

Mid-long term runoff forecast based on GA-SVR

作　　者：王宏伟[1] 张鑫[1] 邱俊楠[1] 孙天青[2] 

机构地区：[1]西北农林科技大学水利与建筑工程学院,陕西杨凌712100 [2]中水北方勘测设计研究有限责任公司,天津300222

出　　处：《西北农林科技大学学报（自然科学版）》2012年第2期201-206,共6页Journal of Northwest A&F University(Natural Science Edition)

基　　金：国家高技术研究发展计划("863"计划)项目(14110209);国家重大科技支撑计划项目(2006BAD11B05);西北农林科技大学博士科研启动基金项目(01140504);西北农林科技大学科研专项(08080230)

摘　　要：【目的】将遗传算法(GA)与支持向量机回归(SVR)2种算法结合,构建GA-SVR模型,并采用该模型对径流进行预报,为制定防洪抗旱与水资源调度方案提供依据。【方法】以陕西府谷县黄甫川水文站1979-2003年实测资料作为拟合样本,2004-2008年资料作为检验样本,选取降水量、蒸发量为输入量,径流为输出量,通过GA优化SVR的结构和参数,建立GA-SVR预报模型,进而进行径流预报,同时与基于误差反向传播算法的人工神经网络(BP-ANN)、投影寻踪回归(PPR)模型的预报结果进行对比分析。【结果】应用GA-SVR、BP-ANN、PPR 3个模型在径流拟合阶段的预报精度较检验阶段有所下降,但是预报精度均达到了乙级水平,其中以GA-SVR的预报精度最高,效果最好。【结论】GA-SVR模型实现了SVR参数自动化选取,较好地解决了高度非线性、小样本、过学习等问题,模型可行有效,为径流预报提供了一种新途径。【Objective】 In order to provide some significant references for the program of flood control and drought resistance and water scheduling,the GA-SVR forecast model established by integrating GA with SVR was utilized to forecast runoff.【Method】 Hydrological data from 1979 to 2003 were chosen as a training sample and the data from 2004 to 2008 as a test sample in Huangpuchuan station in Fugu county,and then precipitation and evaporation were selected as input variables,runoff as a output variable.Structure and parameters of support vector regression were optimized by genetic algorithm,then the GA-SVR forecast model was established and runoff forecast got under way.The results were compared and analyzed with GA-SVR,BP-ANN and PPR.【Result】 The results obtained by GA-SVR,BP-ANN and PPR showed that the precision of fitting was better than test,but both reached grade B.Meanwhile,the precision of GA-SVR was best and its effect was remarkable.【Conclusion】 The GA-SVR achieved parameters automatic selection in application of support vector regression,solved highly nonlinear,small sample and learning problems.Overall,its model and method were feasible and effective,which provided a new way for runoff forecast.

关 键 词：支持向量机回归 遗传算法 径流预报 精度等级 

分 类 号：TV121[水利工程—水文学及水资源]