气候变化领域集成服务门户(CCPortal): Rainfall prediction using optimally pruned extreme learning machines

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DOI	10.1007/s11069-021-04706-9
	Rainfall prediction using optimally pruned extreme learning machines
	Li H.; He Y.; Yang H.; Wei Y.; Li S.; Xu J.
发表日期	2021
ISSN	0921030X
起始页码	799
结束页码	817
卷号	108 期号:1
英文摘要	Rainfall impacts local water quantity and quality. Accurate and timely prediction of rainfall is highly desirable in water management and hydrogeologic hazards mitigation, which is critical for the environmentally sustainable development. Previous rainfall prediction processes are complex and computationally costly for its intrinsic high uncertainty and variability. In this paper, a data-driven approach is applied to predict the rainfall based on historic data via time-series modeling and optimally pruned extreme learning machine (OP-ELM). The rainfall datasets collected from six counties within Three Georges Reservoir are utilized as case studies in this research, first, the rainfall data is pre-processed with outlier removal and missing value imputation, and the monthly ahead difference is computed based on the instant average of monthly rainfall. Next, the autocorrelation function and partial autocorrelation function were computed and the Ljung–Box test statistic is utilized to explore the significance of the historic lagged-series. All the statistically significant historic lags are selected as inputs for prediction algorithms. Last, an OP-ELM algorithm is developed to predict the monthly rainfall with tenfold cross validation. Four activation functions: the sigmoid, sine, hardlim, and radial basis function, are considered in the OP-ELM. The prediction performance is evaluated with metrics including mean absolute error, mean absolute percentage error, root mean square error, and max error rate. Overall, the computational results indicate the proposed framework outperforms the other benchmarking machine learning algorithms through six case studies in Three Gorges Reservoir, China. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	OP-ELM Rainfall prediction Sustainable development Time-series modeling
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206362
作者单位	School of Architecture and Civil Engineering, Chengdu University, Chengdu, Sichuan 610106, China; Department of Mechanical and Industrial Engineering, The University of Iowa, Iowa City, IA 52242, United States; State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu, Sichuan 610059, China; Department of Natural Resources of Guizhou Province, Guiyang, Guizhou 550004, China
推荐引用方式 GB/T 7714	Li H.,He Y.,Yang H.,et al. Rainfall prediction using optimally pruned extreme learning machines[J],2021,108(1).
APA	Li H.,He Y.,Yang H.,Wei Y.,Li S.,&Xu J..(2021).Rainfall prediction using optimally pruned extreme learning machines.Natural Hazards,108(1).
MLA	Li H.,et al."Rainfall prediction using optimally pruned extreme learning machines".Natural Hazards 108.1(2021).