开发用于河水流量曲线预测的功能定量自回归模型

IF 2.9 3区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
Muge Mutis, Ufuk Beyaztas, Gulhayat Golbasi Simsek, Han Lin Shang, Zaher Mundher Yaseen
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引用次数: 0

摘要

在多个水文过程中,河流流量是一个重要参数,对不同的水资源工程活动至关重要。尽管文献中采用了多种方法对河流流量进行建模,但在对河流流量时间序列曲线建模方面仍存在局限性。本研究建立了一个一阶函数量子自回归模型,以描述河流流量时间序列曲线的整个条件分布。在功能主成分分析的基础上,使用多元量级回归框架估算了回归参数函数。为此,使用从澳大利亚三条河流(玛丽河、洛克耶河谷和阿尔伯特河)收集的小时尺度河流流量来评估所建议方法的精细样本性能。对三个站点的一系列蒙特卡洛实验和历史数据集进行了研究。此外,方法评估还采用了不确定性分析。与现有方法相比,所提出的模型对离群观测值、非高斯和重尾误差分布以及异方差提供了更稳健的预测。此外,所提出的模型还具有预测河流流量时间序列在中心和非中心位置的未来实现时间间隔的优点。结果证实,与现有的函数时间序列方法相比,该模型在预测河流流量时间序列曲线方面具有较高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Development of Functional Quantile Autoregressive Model for River Flow Curve Forecasting

Development of Functional Quantile Autoregressive Model for River Flow Curve Forecasting

Among several hydrological processes, river flow is an essential parameter that is vital for different water resources engineering activities. Although several methodologies have been adopted over the literature for modeling river flow, the limitation still exists in modeling the river flow time series curve. In this research, a functional quantile autoregressive of order one model was developed to characterize the entire conditional distribution of the river flow time series curve. Based on the functional principal component analysis, the regression parameter function was estimated using a multivariate quantile regression framework. For this purpose, hourly scale river flow collected from three rivers in Australia (Mary River, Lockyer Valley, and Albert River) were used to evaluate the finite-sample performance of the proposed methodology. A series of Monte-Carlo experiments and historical data sets were examined at three stations. Further, uncertainty analysis was adopted for the methodology evaluation. Compared with the existing methods, the proposed model provides more robust forecasts for outlying observations, non-Gaussian and heavy-tailed error distribution, and heteroskedasticity. Also, the proposed model has the merit of predicting the intervals of future realizations of river flow time series at the central and non-central locations. The results confirmed the potential for predicting the river flow time series curve with a high level of accuracy in comparison with the benchmark existing functional time series methods.

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来源期刊
Earth and Space Science
Earth and Space Science Earth and Planetary Sciences-General Earth and Planetary Sciences
CiteScore
5.50
自引率
3.20%
发文量
285
审稿时长
19 weeks
期刊介绍: Marking AGU’s second new open access journal in the last 12 months, Earth and Space Science is the only journal that reflects the expansive range of science represented by AGU’s 62,000 members, including all of the Earth, planetary, and space sciences, and related fields in environmental science, geoengineering, space engineering, and biogeochemistry.
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