基于PCA-PSO-SVR 的丹江口水库年径流预报研究

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。

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

In the annual runoff fo recast, the auto correlat ion betw een meteo ro log ical facto rs w ill have an impact o n fo recast accu2 r acy. In o rder to so lv e this problem, w e added PCA and PSO to the SVR model and established a PCA2PSO2SVR model. We re2 mo ved the r edundant informat ion and no ise, ext racted the main features of the facto rs, and used the o ptimal parameter combina2 tion of the model as input to t he reg ression support v ecto r machine ( SVR) model. T aking Danjiangko u Reserv oir, the w ater so ur ce of the Middle Route of the South2to2No rth Water Div ersion Project , as the study area, we carried o ut model t ests using the data from 1981 to 2016 in Danjiang ko u. T he results show ed that t he qualificat ion r ate during the model v erificat ion period was 831 33% and the consistency rat e also r eached 831 33% , indicating hig h accuracy and stability. The model has certain r efer2 ence v alue for the annua l runoff fo recast of Danjiang ko u Reser vo ir.