基于XGBoost算法的多元水文时间序列趋势相似性挖掘

doi:10.11896/jsjkx.200500128

计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 459-463.doi: 10.11896/jsjkx.200500128

基于XGBoost算法的多元水文时间序列趋势相似性挖掘

丁武^1,3, 马媛², 杜诗蕾², 李海辰³, 丁公博³, 王超³

1 华中科技大学水电与数字化工程学院武汉 430074
2 太湖流域管理局水文局(信息中心) 上海 200434
3 中国水利水电科学研究院北京 100038

出版日期:2020-11-15 发布日期:2020-11-17
通讯作者: 王超(wangchao@iwhr.com)
作者简介:M201873779@hust.edu.cn
基金资助:
青年人才托举工程(2019QNRC001);中国水利水电科学研究院基本科研业务费专项(WR0145B012020)

Mining Trend Similarity of Multivariate Hydrological Time Series Based on XGBoost Algorithm

DING Wu^1,3, MA Yuan², DU Shi-lei², LI Hai-chen³, DING Gong-bo³, WANG Chao³

1 School of Hydropower and Information Engineering,Hua Zhong University of Science and Technology,Wuhan 430074,China
2 Taihu Basin Authority of Ministry of Water Resources(Information Center),Shanghai 200434,China
3 China Institute of Water Resources and Hydropower Research,Beijing 100038,China

Online:2020-11-15 Published:2020-11-17
About author:DING Wu,born in 1996,postgraduate.His main research interests include hydrological big data analysis and optimized operation of hydropower.
WANG Chao,born in 1989,Ph.D,senior engineer.His main interests research include basin water resources scheduling and intelligent water conservancy.
Supported by:
This work was supported by the Young Elite Scientists Sponsorship Program by the CAST (2019QNRC001)and Fundamental Research Funds of China Institute of Water Resources and Hydropower Research (WR0145B012020).

摘要/Abstract

摘要： 针对传统的利用神经网络等工具进行水文趋势预测得出结果不具备解释性等不足,文中提出一种基于机器学习算法的水文趋势预测方法,该方法旨在利用XGBOOST机器学习算法建立参照期与水文预见期之间各水文特征的相似度映射模型,从而在历史水文时间序列中匹配出与预见期水文趋势最相似的序列,从而达到水文趋势预测的目的。为了证明所提方法的高效性和可行性,以太湖水文时间序列数据为对象进行了验证。分析结果表明,基于机器学习的多元水文时间序列趋势相似性分析可以满足调度人员对未来水文趋势预测效果的要求。

关键词: 多元时间序列, 机器学习, 时间序列数据挖掘, 水文趋势预测, 相似性度量

Abstract: In view of the shortcomings of the traditional hydrological trend prediction using neural networks and other tools,the results are not interpretable and so on.This paper proposes a method of hydrological trend prediction based on machine learning algorithms,which aims to use the XGBOOST machine learning algorithm to establish a similarity mapping model for each hydrological feature between the reference period and the hydrological prediction period,thus,the most similar sequence to the hydrological trend in the foreseeing period is matched in the historical hydrological time series,so as to achieve the purpose of hydrological trend prediction.In order to prove the efficiency and feasibility of the proposed method,it was verified with the Taihu hydrological time series data as the object.The analysis results show that the multi-variable hydrological time series trend simila-rity analysis based on machine learning can meet therequirements of dispatchers for the prediction effect of future hydrological trends.

Key words: Hydrological trend prediction, Machine learning, Multivariate time series, Similarity measure, Time series data mining

中图分类号:

TV121

丁武, 马媛, 杜诗蕾, 李海辰, 丁公博, 王超. 基于XGBoost算法的多元水文时间序列趋势相似性挖掘[J]. 计算机科学, 2020, 47(11A): 459-463. https://doi.org/10.11896/jsjkx.200500128

DING Wu, MA Yuan, DU Shi-lei, LI Hai-chen, DING Gong-bo, WANG Chao. Mining Trend Similarity of Multivariate Hydrological Time Series Based on XGBoost Algorithm[J]. Computer Science, 2020, 47(11A): 459-463. https://doi.org/10.11896/jsjkx.200500128

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基于XGBoost算法的多元水文时间序列趋势相似性挖掘

Mining Trend Similarity of Multivariate Hydrological Time Series Based on XGBoost Algorithm

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