基于PCA-PSO-SVR的丹江口水库年径流预报研究  

Research on annual runoff forecast of Danjiangkou Reservoir based on PCA-PSO-SVR

作　　者：张岩 杨明祥[2] 雷晓辉[2] 舒坚[1] 牛文生[3] 余琅[2] ZHANG Yan;YANG Mingxiang;LEI Xiaohui;SHU Jian;NIU Wensheng;YU Lang(School of Software,Nanchang Hangkong University,Nanchang 330063,China;China Institute of Water Resources and Hydropower Research,Beijing 100038,China;AVIC Xi′an Institute of Aeronautical Computing Technology,Xi′an 710068,China)

机构地区：[1]南昌航空大学软件学院,南昌330063 [2]中国水利水电科学研究院,北京100038 [3]中航工业西安航空计算技术研究所,西安710068

出　　处：《南水北调与水利科技》2018年第5期35-40,共6页South-to-North Water Transfers and Water Science & Technology

基　　金：国家重点研发计划(2016YFC0402201);国家自然科学基金(51709271);青年人才托举工程(2017QNRC001);中国水利水电科学研究院基本科研业务费专项(WR0145B212017);江西省研究生创新专项资金项目(YC2017068)~~

摘　　要：在年径流预报中,气象因子之间的自相关会对预报精度产生影响。针对这个问题,将主成分分析(PCA)和粒子群优化(PSO)算法加入SVR模型中,建立了PCA-PSO-SVR预报模型,剔除冗余信息和噪声,提取因子间的主要特征,并选择模型的最优参数组合作为回归支持向量机(SVR)模型的输入。选择南水北调中线水源地丹江口水库为研究区,利用丹江口1981-2016年入库资料进行模型检验。结果表明,模型验证期间合格率为83.33%、距平一致率也达到83.33%,具有精度高稳定性强等优点,对丹江口水库年径流预报有一定的参考意义。In the annual runoff forecast,the autocorrelation between meteorological factors will have an impact on forecast accuracy.In order to solve this problem,we added PCA and PSO to the SVR model and established a PCA-PSO-SVR model.We removed the redundant information and noise,extracted the main features of the factors,and used the optimal parameter combination of the model as input to the regression support vector machine(SVR)model.Taking Danjiangkou Reservoir,the water source of the Middle Route of the South-to-North Water Diversion Project,as the study area,we carried out model tests using the data from 1981 to 2016 in Danjiangkou.The results showed that the qualification rate during the model verification period was 83.33%and the consistency rate also reached 83.33%,indicating high accuracy and stability.The model has certain reference value for the annual runoff forecast of Danjiangkou Reservoir.

关 键 词：年径流预报 主成分分析 粒子群优化算法 回归支持向量机 丹江口水库 

分 类 号：P333[天文地球—水文科学]