基于样条插值的水沙通量时间序列预测算法  

Time Series Prediction Algorithm of Water and Sediment Flux Based on Spline Interpolation

作　　者：张颖 杨廷尧 ZHANG Ying;YANG Tingyao(Guangdong Preschool Normal College In Maoming,Maoming 525000,China)

机构地区：[1]广东茂名幼儿师范专科学校,广东茂名525000

出　　处：《现代信息科技》2024年第20期145-148,共4页Modern Information Technology

基　　金：茂名市科技局科技计划项目(2024399);广东茂名幼儿师范专科学校2023年度教育科学“十四五”规划课题(2023GMYSKT07);广东省普通高校特色创新类项目(2022KTSCX353)。

摘　　要：对水沙通量变化趋势的预测是沿黄河流域环境治理的基础。鉴于监测技术限制,采集到的含沙量数据相较于水流量数据通常存在大量缺失值,影响对水沙通量变化的精准评估。针对此问题,文章分别采用最邻近、线性、二次样条、三次样条插值方法进行数据补值,并对比插值拟合误差。实验结果表明,采用三次样条插值法进行插值曲线拟合误差最小,经过插值处理后的数据能更好地预测未来水沙通量的变化趋势。The prediction of the variation trend of water and sediment flux is the basis of environmental governance along the Yellow River Basin.In view of the monitoring technology,the collected sediment flux data usually has a large number of missing values compared with the water flux data,which affects the accurate assessment of the variation of water and sediment flux.To solve this problem,the Nearest Neighbor,Linear,Quadratic Spline and Cubic Spline Interpolation methods are used to supplement the data,and the fitting error of the interpolation is compared.The experimental results show that the Cubic Spline Interpolation method is used to minimize the curve error,and the data after interpolation can better predict the future variation trend of water and sediment flux.

关 键 词：大数据处理 样条插值 时间序列 趋势预测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]