基于水文因子深度学习集合建模的径流区间预测方法研究

IF 3.9 3区 环境科学与生态学 Q1 ENGINEERING, CIVIL
Jinghan Huang, Zhaocai Wang, Jinghan Dong, Junhao Wu
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引用次数: 0

摘要

精确预测径流不仅有利于防洪抗旱,也有利于合理利用水资源。由于极端天气频发和径流变化的复杂性,实现精确的径流预测具有挑战性。本研究开发了一种基于气象和水文因素的区间预测深度学习集合模型。该模型可分为四个阶段:特征提取、分解、点预测和区间预测。首先,通过皮尔逊相关系数筛选出影响径流的关键驱动变量。其次,通过变异模态分解(VMD)将原始数据分解为固有模态函数(IMF);然后,通过互补集合经验模态分解(CEEMD)对每个 IMF 进行分解,以捕捉更多的数据细节。然后,通过注意机制融合门控循环单元(AM-GRU)实现径流点预测部分。本研究利用位于中国疏勒河上游和下游的敦煌站和潘家庄站的数据,对 VMD-CEEMD-ISSA-AM-GRU (VCIAG) 模型进行了验证和分析。结果表明:(1)VCIAG 模型拟合效果最好,敦煌站和潘家庄站的 NSE 值分别为 0.97 和 0.96。(2)在多期预报中,预报期为 1 天时预报精度最高,随着预报期的延长,预报精度逐渐降低。(3) 在洪水预警方面,VCIAG 在两个站点的表现都很好,这表明所提出的模型可以在洪水来临之前提前采取预防措施。(4) 在区间预测方面,VCIAG 模型的预测区间宽度最小,预测精度最高,提高了模型的应用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors

Research on runoff interval prediction method based on deep learning ensemble modeling with hydrological factors

Precise prediction of runoff is not only conducive to the prevention of floods and droughts but also to the rational use of water resources. Due to the frequency of weather extremes and the complexity of runoff variability, achieving accurate runoff predictions is challenging. This research develops a deep-learning ensemble model for interval prediction based on meteorological and hydrological factors. The model can be divided into four stages: feature extraction, decomposition, point prediction, and interval prediction. First, Pearson's correlation coefficient filters out key driving variables affecting runoff. Next, the original data are decomposed by variational modal decomposition (VMD) to intrinsic modal function (IMF); Then, each IMF is decomposed by complementary ensemble empirical modal decomposition (CEEMD) to capture more data details. Following, the runoff point prediction portion is realized by the attention mechanism fusion gated recurrent unit (AM-GRU). In this study, data from Dunhuang and Panjiazhuang stations, located in the upper and lower reaches of the Shule River in China, were used to validate and analyze the VMD-CEEMD-ISSA-AM-GRU (VCIAG) model. The results show that (1) the VCIAG model has the best fitting effect which the NSE values of Dunhuang and Panjiazhuang stations are 0.97 and 0.96, respectively. (2) In the multi-period prediction in advance, the highest prediction accuracy is achieved when the prediction period is 1 day and the accuracy of the prediction decreases gradually as the prediction period becomes longer. (3) In flood early warning, the VCIAG performs well at both stations, which suggests that the proposed model can take precautionary measures in advance before the floods come. (4) In terms of interval prediction, the VCIAG model has the narrowest prediction interval width and the highest prediction accuracy, which enhances the application value of the model.

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来源期刊
CiteScore
7.10
自引率
9.50%
发文量
189
审稿时长
3.8 months
期刊介绍: Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas: - Spatiotemporal analysis and mapping of natural processes. - Enviroinformatics. - Environmental risk assessment, reliability analysis and decision making. - Surface and subsurface hydrology and hydraulics. - Multiphase porous media domains and contaminant transport modelling. - Hazardous waste site characterization. - Stochastic turbulence and random hydrodynamic fields. - Chaotic and fractal systems. - Random waves and seafloor morphology. - Stochastic atmospheric and climate processes. - Air pollution and quality assessment research. - Modern geostatistics. - Mechanisms of pollutant formation, emission, exposure and absorption. - Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection. - Bioinformatics. - Probabilistic methods in ecology and population biology. - Epidemiological investigations. - Models using stochastic differential equations stochastic or partial differential equations. - Hazardous waste site characterization.
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