Label distribution learning with climate probability for ensemble forecasting

doi:10.3233/IDA-184446

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Label distribution learning with climate probability for ensemble forecasting
	Yang, Xuebing1 ; Wu, Yajing1,2 ; Zhang, Wensheng1,3 ; Tang, Wei4
发表期刊	INTELLIGENT DATA ANALYSIS
ISSN	1088-467X
	2020
卷号	24 期号:1 页码:69-82
通讯作者	Yang, Xuebing(yangxuebing2013@ia.ac.cn)
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.
关键词	Ensemble forecasting label distribution learning post-processing domain knowledge
DOI	10.3233/IDA-184446
关键词[WOS]	SEASONAL CLIMATE ; WEATHER
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61602482] ; National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61602482]
项目资助者	National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000516758100005
出版者	IOS PRESS
七大方向——子方向分类	机器学习
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38395
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Yang, Xuebing
作者单位	1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Foshan Univ, Sch Math & Big Data, Foshan, Guangdong, Peoples R China 4.Publ Meteorol Serv Ctr China Meteorol Adm, Beijing, 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.