Label distribution learning with climate probability for ensemble forecasting

doi:10.3233/IDA-184446

CORC > 自动化研究所 > 中国科学院自动化研究所 > 精密感知与控制研究中心 > 人工智能与机器学习

	Label distribution learning with climate probability for ensemble forecasting
	Yang, Xuebing 4; Wu, Yajing 1,4; Zhang, Wensheng 2,4; Tang, Wei 3
刊名	INTELLIGENT DATA ANALYSIS
	2020
卷号	24 期号:1 页码:69-82
关键词	Ensemble forecasting label distribution learning post-processing domain knowledge
ISSN号	1088-467X
DOI	10.3233/IDA-184446
通讯作者	Yang, Xuebing(yangxuebing2013@ia.ac.cn)
英文摘要	In meteorology, ensemble forecasting aims to post-process an ensemble of multiple members' forecasts and make better weather predictions. While multiple individual forecasts are generated to represent the uncertain weather system, the performance of ensemble forecasting is unsatisfactory. In this paper we conduct data analysis based on the expertise of human forecasters and introduce a machine learning method for ensemble forecasting. The proposed method, Label Distribution Learning with Climate Probability (LDLCP), can improve the accuracy of both deterministic forecasting and probabilistic forecasting. The LDLCP method utilizes the relevant variables of previous forecasts to construct the feature matrix and applies label distribution learning (LDL) to adjust the probability distribution of ensemble forecast. Our proposal is novel in its specialized target function and appropriate conditional probability function for the ensemble forecasting task, which can optimize the forecasts to be consistent with local climate. Experimental testing is performed on both artificial data and the data set for ensemble forecasting of precipitation in East China from August to November, 2017. Experimental results show that, compared with a baseline method and two state-of-the-art machine learning methods, LDLCP shows significantly better performance on measures of RMSE and average continuous ranked probability score.
资助项目	National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61602482]
WOS关键词	SEASONAL CLIMATE ; WEATHER
WOS研究方向	Computer Science
语种	英语
出版者	IOS PRESS
WOS记录号	WOS:000516758100005
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/38394]
专题	精密感知与控制研究中心_人工智能与机器学习
通讯作者	Yang, Xuebing
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Foshan Univ, Sch Math & Big Data, Foshan, Guangdong, Peoples R China 3.Publ Meteorol Serv Ctr China Meteorol Adm, Beijing, Peoples R China 4.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Xuebing,Wu, Yajing,Zhang, Wensheng,et al. Label distribution learning with climate probability for ensemble forecasting[J]. INTELLIGENT DATA ANALYSIS,2020,24(1):69-82.
APA	Yang, Xuebing,Wu, Yajing,Zhang, Wensheng,&Tang, Wei.(2020).Label distribution learning with climate probability for ensemble forecasting.INTELLIGENT DATA ANALYSIS,24(1),69-82.
MLA	Yang, Xuebing,et al."Label distribution learning with climate probability for ensemble forecasting".INTELLIGENT DATA ANALYSIS 24.1(2020):69-82.