现代信息科技2024,Vol.8Issue(20) :145-148.DOI:10.19850/j.cnki.2096-4706.2024.20.029

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

张颖 杨廷尧
现代信息科技2024,Vol.8Issue(20) :145-148.DOI:10.19850/j.cnki.2096-4706.2024.20.029

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

张颖 1杨廷尧1
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作者信息

  • 1. 广东茂名幼儿师范专科学校,广东 茂名 525000
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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.

Abstract

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

Key words

Big Data processing/Spline Interpolation/time series/trend prediction

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出版年

2024
现代信息科技
广东省电子学会

现代信息科技

ISSN:2096-4706
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