Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11A): 459-463.doi: 10.11896/jsjkx.200500128

• Big Data & Data Science • Previous Articles     Next Articles

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

DING Wu1,3, MA Yuan2, DU Shi-lei2, LI Hai-chen3, DING Gong-bo3, WANG Chao3   

  1. 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).

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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

CLC Number: 

  • TV121
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