Enhancing Streamflow Prediction in Ungauged Basins Using a Nonlinear Knowledge-Based Framework and Deep Learning

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES
Parnian Ghaneei, Ehsan Foroumandi, Hamid Moradkhani
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引用次数: 0

Abstract

In hydrology, a fundamental task involves enhancing the predictive power of a model in ungagged basins by transferring information on physical attributes and hydroclimate dynamics from gauged basins. Introducing an integrated nonlinear clustering framework, this study aims to develop a comprehensive framework that augments predictive performance in basins where direct measurements are sparse or absent. In this framework, uniform manifold approximation and projection (UMAP) is used as a nonlinear method to extract the essential features embedded in hydro-climatological attributes and physical properties. Then, the Growing Neural Gas (GNG) clustering model is used to find the basins that potentially share similar hydro-climatological behaviors. Besides UMAP-GNG, the integration of Principal Component Analysis (PCA) as a linear method to reduce dimensionality with common clustering methods are also assessed to serve as benchmarks. The results reveal that the combination of clustering algorithms with the PCA method may lead to loss of information while the nonlinear method (UMAP) can extract more informative features. The efficacy of the proposed framework is assessed across the Contiguous United States (CONUS) by training a single Base Model using long short-term memory (LSTM) for the centroids of all clusters and then, fine-tuning the model on the centroids of each cluster separately to create a regional model. The results indicate that using the information extracted by the UMAP-GNG method to guide a Base Model can significantly improve the accuracy in most of the clusters and enhance the median prediction accuracy within different clusters from 0.04 to 0.37 of KGE in ungauged basins.
利用非线性知识框架和深度学习加强无测站流域的流量预测
在水文学领域,一项基本任务是通过转移测量流域的物理属性和水文气候动态信息,提高模型在无测量流域的预测能力。本研究引入了一个综合非线性聚类框架,旨在开发一个全面的框架,以提高直接测量数据稀少或缺乏的流域的预测性能。在该框架中,统一流形近似和投影(UMAP)被用作一种非线性方法,用于提取蕴含在水文气候属性和物理特性中的基本特征。然后,利用生长神经气体(GNG)聚类模型找出可能具有相似水文气候行为的盆地。除 UMAP-GNG 外,还评估了将主成分分析(PCA)作为线性降维方法与常见聚类方法相结合的基准。结果表明,将聚类算法与 PCA 方法相结合可能会导致信息丢失,而非线性方法(UMAP)则能提取出更多的信息特征。通过对所有聚类的中心点使用长短期记忆(LSTM)训练单个基础模型,然后分别对每个聚类的中心点进行微调以创建区域模型,评估了所提议的框架在整个毗连美国(CONUS)范围内的功效。结果表明,利用 UMAP-GNG 方法提取的信息来指导基础模型,可以显著提高大多数簇的精度,并将不同簇内的预测精度中位数从 0.04 提高到 0.37。
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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