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

作　　者：丁武 马媛 杜诗蕾 李海辰[3] 丁公博 王超[3] DING Wu;MA Yuan;DU Shi-lei;LI Hai-chen;DING Gong-bo;WANG Chao(School of Hydropower and Information Engineering,Hua Zhong University of Science and Technology,Wuhan 430074,China;Taihu Basin Authority of Ministry of Water Resources(Information Center),Shanghai 200434,China;China Institute of Water Resources and Hydropower Research,Beijing 100038,China)

机构地区：[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　　处：《计算机科学》2020年第S02期459-463,共5页Computer Science

基　　金：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)。

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。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.

关 键 词：机器学习 多元时间序列 水文趋势预测 时间序列数据挖掘 相似性度量 

分 类 号：TV121[水利工程—水文学及水资源] P333[天文地球—水文科学]