摘要
为了解决现有实时校正方法对山区小流域洪水进行校正能力不足的问题,引入K最近邻算法用于洪水预报实时校正。以安徽省沙埠流域为试验流域,构建基于K最近邻算法的实时校正模型,同时采用BP神经网络实时校正法和传统的误差自回归方法,以洪峰相对误差和确定性系数为评价指标,分析各校正模型的校正结果。结果表明:基于K最近邻的实时校正法对确定性系数改善最优,BP神经网络实时校正法对洪峰误差校正更精确;将历史洪水资料纳入学习样本后,基于K最近邻的实时校正法的校正能力将进一步提升。基于K最近邻的实时校正法能够有效避免误差自回归方法对洪峰误差控制较差的缺陷,适应性强,反应灵敏,精确度高,可作为山区小流域洪水预报实时校正的有效工具。
Considering the poor performance of existing real-time correction methods of flood forecasting in small mountain watersheds, this study introduced the K-nearest neighbor algorithm into the real-time correction method of flood forecasting. The real-time correction model based on the K-nearest neighbor algorithm （ the KNN method） was built and the Shabu Basin, in Anhui Province, was chosen as the experimental basin. Meanwhile, the real-time correction method of back-propagation neural networks （ the BP method ） and the traditional error autoregression method （ the AR method） were also used to analyze the correction results of correction models with the evaluation indices of the flood peak relative error and the certainty coefficient. The results showed that the KNN method improved the most on the error correction of flood peak and the BP method was more accurate. The correction ability of the KNN method improved more when the historical flood data were added to the learning sample. The KNN method can effectively avoid the defect of the AR method, that the flood peak error cannot be controlled. The KNN method is well-adapted and sensitive, has high accuracy, and can be used as an effective tool to promote real-time correction in small mountain watersheds.
出处
《河海大学学报（自然科学版）》
CAS
CSCD
北大核心
2015年第3期208-214,共7页
Journal of Hohai University(Natural Sciences)
基金
国家自然科学基金(41130639
41101017
51179045
41201028)
水利部公益项目(201301068)
关键词
实时校正
山区小流域
K最近邻算法
BP神经网络
误差自回归方法
沙埠流域
real-time correction
small mountain watershed
K-nearest neighbor algorithm
back-propagation neural networks
error autoregression method
Shabu Basin
