气候变化领域集成服务门户(CCPortal): Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients

CCPortal

DOI	10.1007/s11269-024-03848-2
	Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients
	Chang, Xinyu; Guo, Jun; Qin, Hui; Huang, Jingwei; Wang, Xinying; Ren, Pingan
发表日期	2024
ISSN	0920-4741
EISSN	1573-1650
英文摘要	Human activities and climate change have exacerbated the frequency of extreme weather events such as rainstorms and floods, which makes it difficult to accurately quantify the uncertainty characteristics in runoff prediction. Therefore, the lower and upper boundary estimation method (LUBE) has become an important means to quantify uncertainty and has been widely used. However, the traditional interval prediction evaluation system only relies on coverage and width indicators, and performs poorly in single-objective optimization methods, which limits the large-scale application of the LUBE method. Based on this, this study innovatively proposes the prediction interval fitting coefficient (PIFC), and combines the prediction interval coverage probability (PICP) and normalized average width index (PINAW) to construct the coverage width fitting-based criterion (CWFC) for the first time, which broadens and improves the interval prediction evaluation dimension system. Further, the single-objective and multi-objective LUBE interval forecasting models based on the randomized weighted particle swarm algorithm (RWPSO) and the non-dominated sorting genetic algorithms III (NSGA-III) are constructed in this study. The verification results of cascade hydropower stations in the Yalong river basin show that the calculation efficiency and prediction effect of the single target interval prediction model are both improved after the introduction of PIFC. Under the CWFC objective function, the PINAW and PIFC indexes in the prediction interval are significantly better, and the PICP gap is smaller. Under multi-objective conditions (PICP, PINAW and PIFC), the Pareto non-inferior solution set can provide more choices for decision makers. During the flood season, PICP can reach more than 93%, PINAW is controlled below 10%, and PIFC can reach more than 0.95. This fully proves that the performance of interval prediction has been significantly improved after the introduction of PIFC, and the research results can provide a new way for basin interval prediction.
英文关键词	LUBE interval prediction; Interval fitting coefficient; Multi-objective optimization; RWPSO; NSGA-III
语种	英语
WOS研究方向	Engineering ; Water Resources
WOS类目	Engineering, Civil ; Water Resources
WOS记录号	WOS:001205247800001
来源期刊	WATER RESOURCES MANAGEMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302537
作者单位	Huazhong University of Science & Technology; Huazhong University of Science & Technology
推荐引用方式 GB/T 7714	Chang, Xinyu,Guo, Jun,Qin, Hui,et al. Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients[J],2024.
APA	Chang, Xinyu,Guo, Jun,Qin, Hui,Huang, Jingwei,Wang, Xinying,&Ren, Pingan.(2024).Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients.WATER RESOURCES MANAGEMENT.
MLA	Chang, Xinyu,et al."Single-Objective and Multi-Objective Flood Interval Forecasting Considering Interval Fitting Coefficients".WATER RESOURCES MANAGEMENT (2024).