气候变化领域集成服务门户(CCPortal): Enhanced monthly streamflow prediction using an input-output bi-decomposition data driven model considering meteorological and climate information

CCPortal

DOI	10.1007/s00477-024-02731-1
	Enhanced monthly streamflow prediction using an input-output bi-decomposition data driven model considering meteorological and climate information
	Guo, Qiucen; Zhao, Xuehua; Zhao, Yuhang; Ren, Zhijing; Wang, Huifang; Cai, Wenjun
发表日期	2024
ISSN	1436-3240
EISSN	1436-3259
英文摘要	Accurate streamflow prediction is significant for water resources management. However, due to the impact of climate change and human activities, accurately identifying the input factors of the streamflow prediction model and achieving high-precision results presents a significant challenge. In this study, past streamflow, meteorological, and climate factors were utilized as inputs to develop a predictive scenario for the bi-decomposition of input factors and streamflow series, i.e. Scenario 3 (S3). Mutual information (MI) was applied to recognize the input factors prediction potential. Based on the predictive potentials, factors were progressively incorporated into the kernel extreme learning machine (KELM) and hybrid kernel extreme learning machine (HKELM) models optimized by the gazelle optimization algorithm (GOA) to ascertain the optimal input configuration for each sub-series. The prediction results of S3-KELM and S3-HKELM models were obtained by reconstructing the optimal prediction results of each sub-series. The monthly streamflow of the upper Fenhe River Basin, which is in the semi-humid and semi-arid climate zone, was selected as a case study. The results indicate that in comparison to both undecomposed and singly decomposed scenarios, the input-output bi-decomposed scenario more accurately identifies the input factors and constructs high-precision prediction models. The Nash-Sutcliffe efficiency (NSE) of both the S3-KELM and S3-HKELM models exceeds 0.85. Specifically, the S3-HKELM model demonstrates superior performance, capable of handling more complex inputs, with its NSE reaching up to 0.93. Importantly, meteorological and climate factors contribute to the accuracy of streamflow predictions across different scenarios.
英文关键词	Monthly streamflow prediction; Multi-factor input; Variational mode decomposition; Kernel extreme learning machine; Hybrid kernel extreme learning machine
语种	英语
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS记录号	WOS:001208645500001
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300558
作者单位	Taiyuan University of Technology
推荐引用方式 GB/T 7714	Guo, Qiucen,Zhao, Xuehua,Zhao, Yuhang,et al. Enhanced monthly streamflow prediction using an input-output bi-decomposition data driven model considering meteorological and climate information[J],2024.
APA	Guo, Qiucen,Zhao, Xuehua,Zhao, Yuhang,Ren, Zhijing,Wang, Huifang,&Cai, Wenjun.(2024).Enhanced monthly streamflow prediction using an input-output bi-decomposition data driven model considering meteorological and climate information.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT.
MLA	Guo, Qiucen,et al."Enhanced monthly streamflow prediction using an input-output bi-decomposition data driven model considering meteorological and climate information".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2024).