摘要
为进一步提高中长期径流预报的精度,从而为水库调度决策及水资源配置管理提供更可靠的信息支撑,针对径流序列的偏态性和非线性特点,将Box-Cox变换与Lasso回归引入支持向量回归(SVR)模型,构建基于Box-Cox变换与Lasso回归的支持向量回归月径流预报模型(BC-LSVR Model),在对原径流序列进行相空间重构与Box-Cox变换的基础上采用Lasso回归筛选预报因子,进行径流预报研究.分别以Min-Max数据标准化方法及灰色(Gray)预报因子筛选法作为对比方法,构建MM-LSVR与BC-GSVR预报模型,对3种模型预报效果进行对比分析.将模型应用于渭河流域林家村等6个主要控制性水文站月径流预报,结果表明:BC-LSVR模型预报效果最好,在验证期,6个站点的预报平均绝对值相对误差(MARE)均小于20%,合格率(QR)均大于0.6,效率系数(Ens)大于0.52;在率定期和验证期,BC-LSVR模型的3项评价指标均优于MM-LSVR和BC-GSVR模型,说明引入Box-Cox变换及Lasso回归有效地提高了SVR模型的预报精度.
In order to improve the accuracy of the medium-long term runoff prediction and provide more reliable information support for reservoir operation decision and water resource allocation management,the Box-Cox transform and Lasso regression methods were introduced into support vector regression(SVR)model according to the skewness and nonlinearity characteristics of runoff series.A new hybrid model based on Box-Cox transform and Lasso regression and support vector regression(SVR)called BC-LSVR was proposed.On the basis of the phase space reconstruction of the original runoff series and Box-Cox normalization transformation,Lasso regression was used to identify the forecast factors,then predicted monthly runoff.The MM-LSVR and BC-GSVR prediction models were constructed by using the Min-Max data standardization method and the Gray forecast factor identify method as comparison methods,respectively,and the comparative analysis of the prediction effect of three models was carried out.The models was applied to six main hydrological stations such as Linjiacun in Weihe basin,etc.The results show that the BC-LSVR model has the best prediction effect.In the validation period,the average absolute relative error(MARE)of the six stations is less than 20%,the qualification rate(QR)is greater than 0.6,and the efficiency coefficient(Ens)is greater than 0.52.In the calibration period and validation period,three evaluation indexes of the BC-LSVR model are superior to those of the MM-LSVR and BC-GSVR models,indicating that the introduction of Box-Cox transformation and Lasso regression can effectively improve the prediction accuracy of the SVR model.This paper is expected to provide an effective method to improve the accuracy of medium-long-term runoff prediction.
作者
康艳
杨沁瑜
张芳琴
宋松柏
KANG Yan;YANG Qinyu;ZHANG Fangqin;SONG Songbai(College of Water Resource and Architectural Engineering,Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas,Ministry of Education,Northwest Agriculture and Forest University,Yangling 721100,China)
出处
《应用基础与工程科学学报》
EI
CSCD
北大核心
2022年第1期27-39,共13页
Journal of Basic Science and Engineering
基金
陕西省水利科技计划项目(2019slkj-14)
国家自然科学基金项目(51409222,52079110)。
